{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1、导包\n",
    "### 2、定义好超参数\n",
    "### 3、数据预处理（拿到数据和数据预处理）\n",
    "### 4、定义好网络和优化器\n",
    "### 5、训练并记录训练的LOSS和准确率\n",
    "### 6、保存模型"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1、导包"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import torch\n",
    "import torch.nn as nn\n",
    "import torch.nn.functional as F\n",
    "import torchvision#torchvision是视觉的一个包，有模型有数据 也有数据的预处理 下面这个transform就是数据的预处理\n",
    "import torchvision.transforms as transforms#\n",
    "import torch.optim as optim\n",
    "import matplotlib.pyplot as plt\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'1.10.0+cu102'"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "torch.__version__"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2、定义好超参数"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 定义超参数\n",
    "BATCH_SIZE = 32\n",
    "nEpochs = 16\n",
    "numPrint = 1000"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "cpu\n"
     ]
    }
   ],
   "source": [
    "# cuda\n",
    "device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n",
    "print(device)#之列是CPU  如果是有GPU的电脑 这里是显示GPU"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3、数据预处理（拿到数据和数据预处理）"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Compose(\n",
      "    Resize(size=(32, 32), interpolation=PIL.Image.BILINEAR)\n",
      "    ToTensor()\n",
      "    Normalize(mean=[0.4914, 0.4822, 0.4465], std=[0.247, 0.243, 0.261])\n",
      ")\n"
     ]
    }
   ],
   "source": [
    "#下面这部分是数据的预处理\n",
    "transform = transforms.Compose([\n",
    "    transforms.Resize((32, 32)),#把图片resize成32x32\n",
    "    transforms.ToTensor(),#把numpy的array变成tensor\n",
    "    #                    RGB三个通道的均值分别是下面这三个数  标准差分别是下面这三个数\n",
    "    transforms.Normalize([0.4914, 0.4822, 0.4465], [0.247, 0.243, 0.261])])#这里进行标准化，按照正态分布的方式进行标准化\n",
    "print(transform)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "torchvision.transforms.transforms.Compose"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(transform)#数据类型都是torchvision的殊绝类型\n",
    "#compose英文是撰写的意思"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Using downloaded and verified file: ./cifar-10-python.tar.gz\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Dataset CIFAR10\n",
       "    Number of datapoints: 50000\n",
       "    Split: train\n",
       "    Root Location: ./\n",
       "    Transforms (if any): Compose(\n",
       "                             Resize(size=(32, 32), interpolation=PIL.Image.BILINEAR)\n",
       "                             ToTensor()\n",
       "                             Normalize(mean=[0.4914, 0.4822, 0.4465], std=[0.247, 0.243, 0.261])\n",
       "                         )\n",
       "    Target Transforms (if any): None"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 加载数据集 (训练集和测试集)        \n",
    "#几个参数 root表示文件夹路径   train表示是train的数据,download=True如果有数据那就不用下载，transform就是上面定义的数据预处理\n",
    "trainset = torchvision.datasets.CIFAR10(root='./', train=True, download=True, transform=transform)\n",
    "trainset#这个数据集是压缩包文件，我在自己的电脑上解压之后是5个batch，然后是空白的，貌似没有软件可以打开它\n",
    "#torchvision常用的数据集已经封装好了 不用我们去下载\n",
    "#通过打印这个trainset来看 是有50000条数据，然后transform和上面的也是对应的"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "torchvision.datasets.cifar.CIFAR10"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(trainset)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['airplane',\n",
       " 'automobile',\n",
       " 'bird',\n",
       " 'cat',\n",
       " 'deer',\n",
       " 'dog',\n",
       " 'frog',\n",
       " 'horse',\n",
       " 'ship',\n",
       " 'truck']"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "trainset.classes#这里就是看一下  数据集里面的 图片有哪些类型  看到了 有下面10个分类"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": true,
    "jupyter": {
     "outputs_hidden": true
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "50000\n",
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7, 7, 4, 9, 5, 6, 7, 2, 7, 5, 2, 4, 8, 4, 7, 1, 6, 3, 4, 8, 1, 9, 4, 4, 5, 4, 5, 0, 7, 0, 0, 8, 9, 1, 9, 5, 7, 0, 0, 8, 3, 7, 6, 6, 8, 5, 2, 1, 4, 1, 3, 0, 1, 3, 3, 2, 7, 6, 6, 8, 4, 2, 6, 3, 7, 2, 8, 6, 7, 9, 3, 0, 7, 2, 2, 6, 0, 6, 6, 2, 9, 0, 8, 3, 8, 9, 0, 5, 1, 0, 4, 2, 9, 3, 8, 8, 8, 9, 2, 4, 3, 7, 0, 9, 1, 1, 8, 6, 7, 2, 9, 1, 0, 9, 6, 5, 8, 1, 7, 5, 6, 5, 4, 9, 4, 4, 5, 6, 8, 0, 6, 2, 5, 8, 9, 8, 6, 5, 5, 1, 6, 5, 3, 0, 4, 8, 9, 7, 5, 6, 5, 8, 4, 5, 4, 1, 5, 4, 0, 2, 2, 6, 3, 5, 5, 4, 5, 4, 8, 5, 6, 4, 5, 5, 1, 2, 3, 2, 9, 1, 6, 3, 8, 7, 2, 5, 4, 8, 9, 5, 4, 8, 5, 3, 3, 1, 6, 2, 0, 0, 8, 5, 8, 5, 8, 1, 9, 0, 8, 9, 4, 3, 3, 5, 3, 1, 9, 5, 2, 9, 8, 6, 4, 6, 6, 0, 2, 6, 1, 8, 5, 5, 5, 8, 6, 4, 6, 6, 0, 6, 8, 0, 0, 7, 8, 5, 9, 9, 6, 3, 4, 1, 0, 0, 1, 8, 7, 5, 4, 5, 4, 4, 6, 5, 8, 8, 7, 2, 5, 4, 9, 0, 6, 0, 5, 6, 2, 4, 5, 7, 5, 1, 3, 8, 2, 3, 6, 5, 7, 9, 6, 3, 5, 9, 5, 8, 4, 4, 4, 0, 7, 8, 0, 5, 3, 1, 3, 3, 9, 1, 5, 2, 5, 9, 5, 4, 7, 6, 5, 2, 9, 6, 0, 8, 2, 5, 6, 1, 4, 4, 9, 3, 9, 9, 6, 0, 0, 4, 6, 0, 2, 7, 8, 6, 5, 8, 5, 1, 3, 2, 3, 1, 9, 6, 3, 8, 1, 4, 0, 2, 7, 6, 1, 7, 2, 4, 3, 5, 0, 5, 4, 0, 4, 8, 5, 3, 3, 7, 9, 0, 6, 4, 8, 4, 4, 5, 6, 7, 4, 2, 8, 2, 9, 9, 7, 4, 9, 8, 2, 3, 2, 3, 1, 5, 7, 4, 4, 5, 7, 3, 2, 3, 6, 7, 7, 2, 7, 3, 1, 2, 0, 9, 2, 8, 5, 4, 0, 1, 6, 8, 0, 1, 5, 6, 4, 5, 4, 6, 4, 7, 8, 4, 7, 9, 0, 9, 8, 5, 4, 2, 7, 2, 2, 1, 6, 0, 9, 9, 2, 6, 3, 8, 4, 4, 7, 1, 3, 4, 3, 3, 7, 2, 8, 1, 7, 2, 2, 8, 9, 8, 1, 3, 1, 4, 5, 0, 1, 8, 7, 2, 1, 6, 8, 5, 1, 0, 0, 4, 0, 3, 3, 3, 2, 2, 5, 2, 5, 7, 4, 6, 5, 8, 9, 8, 0, 9, 0, 9, 7, 7, 2, 0, 5, 7, 8, 9, 8, 3, 0, 5, 0, 5, 1, 8, 5, 4, 3, 3, 2, 4, 5, 6, 7, 2, 7, 3, 6, 0, 4, 1, 9, 2, 8, 4, 0, 4, 1, 7, 8, 7, 6, 5, 1, 8, 1, 4, 1, 5, 9, 6, 0, 7, 2, 2, 1, 1, 9, 4, 3, 3, 6, 6, 0, 4, 6, 1, 2, 9, 7, 4, 4, 4, 9, 8, 4, 8, 1, 0, 1, 8, 4, 2, 2, 0, 3, 1, 3, 4, 8, 1, 0, 0, 6, 4, 5, 6, 9, 0, 1, 6, 1, 4, 7, 0, 6, 1, 0, 5, 6, 1, 3, 2, 8, 7, 4, 3, 0, 5, 2, 1, 4, 4, 6, 3, 2, 2, 6, 4, 3, 9, 2, 5, 9, 1, 2, 8, 4, 5, 4, 2, 1, 2, 5, 5, 4, 5, 6, 7, 2, 3, 4, 2, 5, 1, 5, 1, 7, 6, 2, 5, 4, 5, 1, 0, 9, 8, 3, 5, 5, 1, 8, 2, 7, 0, 9, 1, 0, 3, 7, 5, 6, 5, 4, 8, 0, 3, 5, 0, 7, 7, 4, 7, 9, 3, 3, 0, 7, 7, 2, 1, 4, 3, 5, 3, 8, 4, 3, 4, 0, 2, 9, 0, 6, 4, 6, 6, 0, 4, 4, 3, 8, 4, 7, 3, 9, 2, 4, 2, 0, 6, 7, 4, 8, 8, 0, 3, 7, 7, 1, 9, 1, 3, 5, 2, 2, 7, 8, 1, 6, 4, 6, 2, 9, 6, 8, 8, 4, 3, 8, 6, 2, 9, 0, 1, 0, 9, 8, 0, 6, 0, 3, 5, 5, 6, 1, 3, 3, 5, 5, 9, 9, 9, 4, 5, 6, 9, 0, 7, 1, 8, 2, 3, 9, 2, 8, 0, 2, 2, 7, 0, 1, 0, 5, 5, 8, 2, 4, 8, 9, 0, 2, 3, 5, 4, 6, 5, 6, 7, 6, 0, 9, 4, 2, 7, 5, 5, 0, 4, 4, 2, 4, 9, 3, 7, 9, 9, 6, 0, 2, 6, 6, 2, 5, 7, 7, 6, 2, 4, 8, 4, 0, 2, 5, 1, 6, 4, 2, 0, 0, 3, 4, 2, 0, 4, 0, 6, 2, 2, 9, 8, 7, 4, 7, 3, 3, 7, 6, 4, 0, 2, 1, 8, 5, 2, 6, 8, 9, 1, 1, 6, 0, 6, 2, 1, 3, 9, 6, 8, 2, 6, 5, 7, 8, 5, 7, 6, 0, 7, 3, 1, 8, 9, 2, 6, 6, 5, 9, 1, 8, 8, 0, 0, 4, 2, 5, 8, 5, 5, 2, 4, 3, 7, 0, 7, 4, 2, 9, 0, 4, 7, 7, 9, 6, 6, 0, 4, 1, 1, 3, 3, 2, 0, 6, 7, 5, 3, 6, 5, 4, 1, 3, 8, 4, 1, 2, 0, 7, 6, 9, 2, 5, 6, 1, 1, 8, 6, 5, 5, 6, 5, 2, 3, 5, 3, 7, 9, 0, 9, 1, 8, 3, 1, 2, 5, 1, 0, 7, 2, 1, 8, 7, 3, 0, 8, 9, 4, 1, 7, 7, 8, 0, 6, 4, 9, 4, 4, 9, 4, 0, 9, 9, 4, 4, 4, 9, 2, 7, 9, 2, 7, 5, 0, 7, 4, 1, 6, 4, 7, 6, 3, 4, 1, 0, 8, 6, 4, 7, 2, 0, 6, 5, 4, 6, 1, 5, 5, 2, 3, 5, 0, 7, 8, 3, 6, 8, 3, 1, 4, 5, 3, 7, 5, 1, 4, 6, 7, 0, 9, 2, 6, 0, 6, 1, 4, 9, 5, 6, 9, 4, 2, 1, 4, 4, 1, 9, 5, 1, 4, 0, 1, 8, 4, 1, 7, 8, 5, 6, 6, 8, 8, 1, 7, 0, 5, 9, 0, 5, 6, 0, 2, 1, 7, 9, 7, 2, 2, 9, 3, 0, 0, 9, 8, 9, 6, 9, 6, 3, 3, 9, 6, 2, 2, 9, 5, 4, 0, 4, 6, 5, 3, 8, 4, 7, 7, 9, 4, 1, 9, 3, 2, 6, 9, 3, 8, 4, 8, 5, 0, 5, 7, 2, 4, 6, 7, 4, 4, 3, 2, 9, 0, 1, 3, 2, 5, 2, 5, 1, 9, 8, 3, 2, 7, 7, 1, 2, 1, 6, 2, 7, 2, 8, 5, 2, 7, 9, 0, 9, 1, 7, 8, 4, 4, 0, 9, 1, 0, 9, 8, 1, 2, 4, 5, 3, 5, 3, 7, 5, 3, 3, 2, 3, 6, 7, 6, 9, 9, 8, 5, 2, 5, 8, 9, 3, 0, 9, 1, 9, 2, 9, 2, 7, 3, 7, 0, 4, 1, 7, 9, 1, 2, 5, 9, 3, 3, 3, 3, 5, 0, 4, 7, 0, 0, 6, 3, 0, 5, 9, 2, 9, 5, 2, 0, 0, 7, 7, 9, 6, 1, 3, 1, 8, 0, 3, 7, 8, 4, 6, 7, 0, 1, 3, 6, 8, 3, 3, 5, 9, 4, 0, 4, 5, 1, 3, 5, 9, 6, 7, 8, 1, 4, 3, 6, 1, 0, 3, 2, 3, 4, 3, 7, 9, 0, 2, 1, 5, 2, 6, 5, 6, 9, 8, 4, 9, 7, 0, 5, 4, 0, 3, 8, 0, 5, 8, 3, 9, 5, 5, 0, 7, 3, 0, 6, 3, 7, 3, 0, 0, 0, 6, 8, 0, 2, 9, 7, 0, 5, 6, 3, 8, 6, 0, 6, 9, 6, 0, 0, 4, 6, 0, 8, 3, 1, 7, 6, 7, 3, 5, 9, 2, 4, 1, 2, 5, 3, 6, 5, 6, 5, 4, 3, 2, 1, 1, 1, 1, 7, 1, 1, 0, 2, 9, 9, 5, 6, 6, 9, 9, 2, 1, 1, 4, 0, 5, 9, 2, 4, 2, 8, 1, 6, 6, 8, 8, 5, 8, 1, 8, 3, 8, 0, 2, 4, 8, 5, 0, 7, 1, 0, 9, 5, 5, 7, 4, 2, 3, 5, 1, 4, 8, 0, 5, 0, 2, 0, 7, 4, 1, 9, 2, 0, 5, 5, 9, 8, 3, 8, 1, 6, 8, 6, 6, 6, 4, 1, 3, 9, 6, 2, 0, 7, 8, 1, 9, 3, 6, 6, 9, 3, 3, 7, 1, 1, 1, 7, 2, 4, 0, 4, 3, 8, 3, 0, 4, 9, 1, 9, 2, 1, 5, 2, 4, 5, 3, 3, 5, 2, 5, 0, 0, 7, 5, 4, 1, 6, 2, 3, 8, 3, 2, 0, 1, 2, 3, 9, 1, 5, 8, 6, 8, 6, 8, 4, 9, 4, 5, 4, 2, 2, 5, 5, 2, 9, 8, 3, 7, 4, 7, 4, 6, 0, 9, 1, 0, 0, 3, 5, 1, 7, 7, 7, 6, 3, 3, 6, 1, 4, 8, 1, 0, 9, 5, 7, 2, 4, 2, 7, 1, 0, 7, 4, 2, 1, 6, 6, 2, 4, 2, 1, 0, 9, 6, 7, 8, 7, 8, 7, 1, 0, 0, 3, 7, 0, 8, 9, 6, 1, 6, 0, 9, 7, 8, 2, 3, 8, 8, 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2, 7, 6, 9, 5, 5, 8, 1, 4, 2, 9, 8, 4, 6, 6, 9, 0, 3, 7, 3, 3, 2, 6, 2, 2, 8, 8, 8, 3, 5, 5, 3, 6, 2, 0, 9, 9, 9, 4, 2, 4, 4, 7, 1, 6, 1, 0, 8, 6, 6, 4, 8, 3, 8, 6, 2, 6, 5, 7, 3, 1, 8, 4, 0, 6, 8, 6, 1, 2, 4, 1, 1, 5, 3, 5, 0, 6, 7, 7, 7, 3, 0, 8, 9, 3, 0, 6, 7, 8, 7, 1, 1, 0, 1, 0, 6, 9, 4, 5, 2, 9, 9, 5, 7, 2, 2, 5, 4, 5, 5, 6, 3, 3, 8, 4, 0, 0, 2, 0, 8, 0, 4, 4, 9, 6, 5, 3, 5, 0, 8, 4, 0, 6, 5, 6, 6, 0, 9, 3, 2, 4, 3, 9, 9, 9, 6, 4, 4, 1, 3, 0, 0, 7, 0, 4, 8, 6, 0, 7, 2, 5, 1, 8, 4, 1, 6, 9, 5, 6, 0, 9, 3, 1, 5, 6, 6, 3, 7, 4, 1, 1, 9, 5, 1, 8, 8, 4, 6, 5, 4, 6, 1, 3, 6, 4, 8, 7, 2, 0, 6, 1, 0, 1, 5, 8, 3, 2, 5, 5, 5, 4, 5, 9, 9, 7, 1, 5, 7, 7, 6, 1, 3, 4, 3, 5, 0, 0, 6, 3, 5, 9, 6, 1, 9, 7, 3, 2, 9, 7, 8, 8, 1, 9, 2, 6, 6, 7, 5, 9, 5, 5, 4, 4, 9, 5, 2, 3, 5, 5, 3, 1, 5, 8, 8, 8, 4, 2, 8, 6, 7, 7, 3, 1, 9, 7, 1, 1, 2, 9, 1, 8, 1, 6, 1, 3, 7, 9, 0, 7, 0, 6, 6, 6, 1, 5, 2, 6, 1, 2, 7, 2, 5, 6, 3, 3, 5, 4, 7, 0, 4, 1, 5, 6, 9, 0, 5, 3, 4, 0, 0, 4, 6, 3, 6, 5, 7, 4, 9, 9, 2, 2, 6, 1, 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4, 1, 0, 6, 7, 4, 9, 9, 5, 5, 9, 6, 2, 3, 5, 1, 5, 9, 4, 0, 1, 4, 3, 1, 1, 8, 7, 0, 1, 5, 9, 8, 9, 7, 6, 2, 9, 0, 9, 1, 1, 3, 6, 6, 5, 7, 9, 0, 6, 6, 5, 1, 8, 1, 8, 0, 9, 5, 5, 1, 2, 0, 8, 7, 9, 1, 9, 5, 7, 5, 3, 0, 7, 4, 8, 0, 2, 5, 2, 7, 9, 7, 8, 4, 8, 5, 2, 2, 5, 9, 1, 4, 5, 1, 6, 5, 2, 9, 7, 5, 4, 7, 9, 3, 7, 2, 3, 6, 5, 8, 8, 7, 1, 0, 5, 4, 7, 2, 9, 2, 5, 8, 4, 2, 1, 7, 3, 9, 8, 2, 1, 6, 0, 1, 5, 9, 8, 6, 5, 8, 8, 8, 3, 7, 4, 3, 3, 3, 2, 6, 9, 7, 3, 9, 6, 6, 3, 7, 8, 5, 3, 0, 0, 2, 4, 4, 0, 2, 2, 5, 5, 4, 0, 6, 2, 1, 0, 8, 8, 6, 4, 5, 3, 1, 2, 4, 6, 7, 6, 8, 1, 4, 3, 9, 9, 7, 4, 7, 3, 7, 9, 5, 1, 6, 1, 8, 5, 7, 4, 4, 7, 9, 5, 7, 3, 2, 5, 2, 3, 2, 8, 7, 8, 9, 1, 6, 0, 5, 0, 1, 3, 0, 7, 3, 0, 0, 0, 7, 6, 6, 0, 7, 7, 0, 4, 0, 4, 9, 8, 7, 6, 3, 9, 0, 2, 8, 8, 2, 8, 4, 4, 8, 4, 4, 3, 4, 5, 5, 4, 2, 6, 9, 6, 4, 5, 0, 4, 2, 2, 6, 4, 6, 1, 1, 5, 1, 4, 7, 6, 8, 2, 3, 0, 3, 5, 8, 3, 6, 3, 1, 9, 1, 7, 9, 8, 9, 2, 7, 0, 6, 0, 7, 3, 6, 0, 3, 6, 3, 5, 5, 8, 4, 7, 0, 6, 6, 6, 8, 2, 4, 3, 6, 7, 9, 4, 2, 3, 8, 1, 4, 6, 3, 0, 8, 7, 4, 1, 9, 8, 8, 3, 5, 0, 4, 1, 0, 1, 2, 6, 1, 3, 6, 0, 0, 5, 6, 9, 6, 8, 4, 0, 9, 2, 2, 8, 5, 5, 3, 2, 1, 9, 4, 9, 3, 8, 9, 4, 9, 0, 5, 0, 8, 2, 3, 7, 8, 9, 4, 5, 3, 2, 5, 0, 7, 2, 1, 1, 4, 4, 8, 9, 4, 6, 9, 4, 9, 3, 5, 6, 9, 9, 7, 7, 6, 1, 6, 8, 8, 3, 1, 6, 7, 7, 2, 0, 7, 5, 2, 3, 4, 6, 9, 7, 6, 1, 6, 6, 7, 9, 9, 1, 0, 2, 2, 5, 7, 9, 9, 9, 1, 6, 4, 3, 3, 1, 3, 6, 3, 1, 1, 9, 9, 6, 3, 8, 0, 0, 2, 6, 6, 1, 4, 4, 2, 0, 2, 8, 7, 2, 5, 2, 0, 8, 7, 6, 5, 9, 5, 4, 8, 7, 3, 5, 6, 5, 1, 1, 9, 9, 9, 7, 8, 0, 6, 0, 3, 7, 7, 8, 6, 1, 1, 4, 7, 3, 7, 3, 3, 8, 6, 3, 9, 1, 0, 1, 1, 8, 2, 4, 5, 2, 3, 9, 2, 8, 8, 7, 1, 0, 8, 8, 9, 0, 0, 3, 6, 5, 1, 0, 7, 0, 8, 3, 2, 8, 9, 8, 7, 8, 5, 1, 4, 5, 0, 3, 8, 6, 9, 9, 8, 1, 3, 7, 4, 6, 8, 7, 7, 4, 0, 9, 4, 9, 6, 6, 9, 6, 4, 6, 5, 8, 9, 3, 0, 8, 1, 3, 3, 2, 1, 7, 3, 9, 7, 1, 7, 1, 3, 1, 8, 0, 4, 9, 1, 3, 7, 5, 4, 9, 0, 7, 8, 2, 9, 8, 4, 4, 8, 5, 0, 2, 8, 8, 5, 9, 6, 7, 0, 7, 6, 9, 5, 2, 1, 7, 5, 7, 3, 2, 2, 0, 7, 9, 6, 3, 7, 2, 1, 9, 6, 1, 9, 5, 6, 2, 8, 4, 7, 5, 9, 6, 2, 3, 3, 9, 5, 6, 0, 5, 6, 3, 6, 0, 7, 7, 6, 6, 1, 1, 5, 5, 4, 4, 3, 6, 8, 3, 7, 5, 3, 1, 5, 2, 2, 8, 2, 7, 5, 5, 7, 3, 1, 9, 0, 0, 7, 2, 6, 0, 1, 0, 8, 1, 0, 7, 6, 4, 6, 1, 4, 1, 2, 8, 0, 9, 6, 1, 7, 6, 4, 3, 3, 3, 9, 9, 0, 8, 0, 8, 0, 1, 7, 8, 8, 5, 1, 6, 1, 3, 9, 3, 0, 8, 8, 4, 7, 1, 8, 6, 2, 7, 2, 9, 5, 8, 1, 4, 9, 6, 9, 6, 7, 3, 1, 0, 1, 6, 7, 4, 8, 1, 9, 6, 2, 0, 4, 5, 0, 3, 9, 1, 9, 5, 8, 3, 5, 2, 6, 8, 1, 1, 5, 1, 0, 6, 0, 9, 3, 3, 8, 8, 1, 3, 2, 2, 7, 5, 9, 7, 5, 7, 2, 2, 6, 9, 9, 6, 3, 6, 8, 3, 5, 3, 1, 7, 2, 5, 8, 5, 4, 7, 9, 3, 8, 4, 3, 5, 1, 5, 7, 1, 1, 4, 1, 0, 1, 3, 5, 1, 5, 6, 0, 0, 3, 3, 8, 9, 1, 4, 1, 4, 0, 5, 1, 0, 0, 6, 0, 5, 4, 6, 0, 4, 5, 9, 8, 5, 5, 7, 0, 1, 2, 0, 7, 0, 5, 8, 6, 9, 0, 2, 6, 2, 1, 9, 5, 8, 8, 3, 3, 9, 5, 5, 2, 6, 8, 5, 8, 1, 0, 0, 0, 2, 0, 2, 1, 1, 3, 4, 5, 1, 8, 8, 1, 8, 8, 1, 2, 3, 4, 3, 5, 7, 5, 9, 6, 3, 9, 6, 1, 4, 1, 1, 1, 1, 8, 7, 1, 1, 4, 9, 4, 4, 3, 3, 9, 3, 6, 1, 4, 7, 6, 4, 3, 7, 9, 5, 1, 3, 0, 1, 2, 3, 3, 7, 6, 4, 6, 6, 8, 3, 7, 0, 1, 7, 8, 3, 0, 9, 0, 6, 9, 5, 6, 7, 0, 9, 1, 5, 4, 3, 5, 7, 5, 7, 0, 1, 6, 8, 6, 0, 8, 1, 7, 0, 5, 9, 1, 9, 5, 2, 6, 3, 4, 9, 0, 0, 7, 1, 5, 1, 8, 2, 8, 5, 8, 5, 2, 2, 4, 2, 4, 9, 7, 4, 3, 3, 2, 9, 7, 8, 8, 6, 4, 2, 3, 1, 5, 6, 1, 7, 5, 6, 5, 2, 0, 8, 3, 5, 5, 1, 0, 4, 4, 5, 2, 5, 0, 2, 2, 3, 4, 7, 5, 2, 7, 9, 1, 0, 0, 2, 7, 7, 0, 4, 3, 3, 4, 8, 7, 8, 6, 1, 4, 9, 4, 8, 5, 6, 2, 2, 8, 4, 1, 7, 0, 3, 7, 8, 7, 9, 8, 1, 1, 3, 6, 7, 3, 7, 3, 6, 1, 8, 9, 9, 2, 8, 9, 2, 9, 3, 1, 8, 8, 3, 3, 3, 9, 7, 1, 7, 5, 6, 0, 2, 7, 2, 5, 6, 4, 4, 3, 6, 7, 5, 7, 4, 6, 7, 7, 9, 2, 6, 3, 1, 8, 3, 9, 4, 3, 5, 8, 8, 6, 8, 9, 3, 9, 6, 3, 6, 0, 8, 3, 3, 7, 9, 7, 9, 5, 7, 1, 5, 1, 7, 8, 8, 6, 6, 3, 0, 8, 2, 5, 9, 8, 6, 0, 9, 8, 5, 5, 0, 0, 2, 1, 3, 1, 6, 1, 8, 2, 9, 9, 9, 2, 5, 5, 5, 7, 1, 8, 2, 9, 5, 0, 6, 7, 1, 4, 3, 8, 4, 8, 3, 3, 4, 7, 9, 8, 3, 7, 9, 6, 8, 9, 7, 1, 2, 8, 8, 9, 3, 1, 5, 0, 3, 6, 8, 7, 9, 5, 6, 6, 0, 9, 1, 1, 3, 7, 8, 1, 5, 7, 3, 0, 7, 7, 3, 9, 4, 4, 6, 6, 4, 4, 6, 8, 4, 8, 2, 5, 3, 1, 8, 7, 6, 9, 5, 7, 6, 8, 8, 2, 3, 9, 2, 1, 4, 3, 8, 1, 7, 3, 5, 4, 3, 3, 4, 8, 7, 2, 5, 1, 4, 2, 0, 1, 0, 2, 6, 9, 1, 1]\n"
     ]
    }
   ],
   "source": [
    "print(len(trainset.targets))#对应的50000个数据 有50000个标签\n",
    "print(trainset.targets)#这里应该对应的是每一个标签"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'airplane': 0,\n",
       " 'automobile': 1,\n",
       " 'bird': 2,\n",
       " 'cat': 3,\n",
       " 'deer': 4,\n",
       " 'dog': 5,\n",
       " 'frog': 6,\n",
       " 'horse': 7,\n",
       " 'ship': 8,\n",
       " 'truck': 9}"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#先隐藏了吧  否则太长了\n",
    "trainset.class_to_idx#z这里看到了每个分类对应的编号   "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'numpy.ndarray'>\n"
     ]
    }
   ],
   "source": [
    "cifatData=trainset.data\n",
    "print(type(cifatData))#通过data这个特征可以来看到 这个数据的对应的图片吧"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(50000, 32, 32, 3)"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cifatData.shape#50000个图片 每个图片是32 32的  毕竟经过那个transform了"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'numpy.ndarray'> \n",
      " [[[ 59  62  63]\n",
      "  [ 43  46  45]\n",
      "  [ 50  48  43]\n",
      "  ...\n",
      "  [158 132 108]\n",
      "  [152 125 102]\n",
      "  [148 124 103]]\n",
      "\n",
      " [[ 16  20  20]\n",
      "  [  0   0   0]\n",
      "  [ 18   8   0]\n",
      "  ...\n",
      "  [123  88  55]\n",
      "  [119  83  50]\n",
      "  [122  87  57]]\n",
      "\n",
      " [[ 25  24  21]\n",
      "  [ 16   7   0]\n",
      "  [ 49  27   8]\n",
      "  ...\n",
      "  [118  84  50]\n",
      "  [120  84  50]\n",
      "  [109  73  42]]\n",
      "\n",
      " ...\n",
      "\n",
      " [[208 170  96]\n",
      "  [201 153  34]\n",
      "  [198 161  26]\n",
      "  ...\n",
      "  [160 133  70]\n",
      "  [ 56  31   7]\n",
      "  [ 53  34  20]]\n",
      "\n",
      " [[180 139  96]\n",
      "  [173 123  42]\n",
      "  [186 144  30]\n",
      "  ...\n",
      "  [184 148  94]\n",
      "  [ 97  62  34]\n",
      "  [ 83  53  34]]\n",
      "\n",
      " [[177 144 116]\n",
      "  [168 129  94]\n",
      "  [179 142  87]\n",
      "  ...\n",
      "  [216 184 140]\n",
      "  [151 118  84]\n",
      "  [123  92  72]]]\n"
     ]
    }
   ],
   "source": [
    "#有了这个数据 就可以去拿到每个每个数据来看看啦\n",
    "print(type(cifatData[0]),'\\n',cifatData[0])#既然是这个ndarry数据类型  拿就可以拿出来显示了"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{0: 'airplane',\n",
       " 1: 'automobile',\n",
       " 2: 'bird',\n",
       " 3: 'cat',\n",
       " 4: 'deer',\n",
       " 5: 'dog',\n",
       " 6: 'frog',\n",
       " 7: 'horse',\n",
       " 8: 'ship',\n",
       " 9: 'truck'}"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import cv2\n",
    "#显示图片，专门显示一个函数吧\n",
    "def show_pic(img,title):\n",
    "    \"\"\"输入这个图片的矩阵 然后显示出来 标题是title\"\"\"\n",
    "    # pass\n",
    "    fig, ax = plt.subplots()\n",
    "    # ax.axis('off')\n",
    "    ax.imshow(cv2.cvtColor(img, cv2.COLOR_BGR2RGB))\n",
    "    plt.title(title)\n",
    "    plt.show()\n",
    "    \n",
    "#把之前的字典的key和value反过来一下  然后可以去通过数字拿到对应的标签\n",
    "new_dict = {v : k for k, v in trainset.class_to_idx.items()}\n",
    "new_dict#这样去show图片的时候 就可以去看到标签了"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'numpy.ndarray'> 50000 \n",
      " <class 'list'> 50000\n"
     ]
    }
   ],
   "source": [
    "#这样构成一个key和value反过来的字典 就是待会可以通过索引去 拿到图片和标题\n",
    "#分别用两个变量 去拿到对应的数据和标签吧\n",
    "train_data=trainset.data\n",
    "train_target=trainset.targets\n",
    "print(type(train_data),len(train_data),\"\\n\",type(train_target),len(train_target))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "6 \t frog \n",
      " [[[ 59  62  63]\n",
      "  [ 43  46  45]\n",
      "  [ 50  48  43]\n",
      "  ...\n",
      "  [158 132 108]\n",
      "  [152 125 102]\n",
      "  [148 124 103]]\n",
      "\n",
      " [[ 16  20  20]\n",
      "  [  0   0   0]\n",
      "  [ 18   8   0]\n",
      "  ...\n",
      "  [123  88  55]\n",
      "  [119  83  50]\n",
      "  [122  87  57]]\n",
      "\n",
      " [[ 25  24  21]\n",
      "  [ 16   7   0]\n",
      "  [ 49  27   8]\n",
      "  ...\n",
      "  [118  84  50]\n",
      "  [120  84  50]\n",
      "  [109  73  42]]\n",
      "\n",
      " ...\n",
      "\n",
      " [[208 170  96]\n",
      "  [201 153  34]\n",
      "  [198 161  26]\n",
      "  ...\n",
      "  [160 133  70]\n",
      "  [ 56  31   7]\n",
      "  [ 53  34  20]]\n",
      "\n",
      " [[180 139  96]\n",
      "  [173 123  42]\n",
      "  [186 144  30]\n",
      "  ...\n",
      "  [184 148  94]\n",
      "  [ 97  62  34]\n",
      "  [ 83  53  34]]\n",
      "\n",
      " [[177 144 116]\n",
      "  [168 129  94]\n",
      "  [179 142  87]\n",
      "  ...\n",
      "  [216 184 140]\n",
      "  [151 118  84]\n",
      "  [123  92  72]]]\n"
     ]
    }
   ],
   "source": [
    "#这不就对应上了吗  然后可以通过随机的去看一些图片已经对应的标签 用之前的写好的函数\n",
    "print(train_target[0],'\\t',new_dict[train_target[0]],'\\n',train_data[0])#嗯  可以写一个函数 随机的去显示一些图片"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "show_pic(train_data[0],new_dict[train_target[0]])#好的 然后就这样随机的显示10章图片吧"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "39428\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "30915\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "8089\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "36140\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "43359\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "49988\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "9653\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "21464\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "16403\n"
     ]
    },
    {
     "data": {
      "image/png": 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/pba5OZ5AsxCpu1ZfDNeFi9VqY+2YAGAhskM+O8sfT/a8B4BSKZx4MzzMFZQtW4g6wUNCV3YhUkHBLkQiKNiFSAQFuxCJoGAXIhEU7EIkQstr0LWXwjJae6QGXYHY8jkun0wP9VPb/AxvW3R+gMsdo+dOB8cXarzO3JETXJbr3cvlsOzZ89T2tYd44sq1+8K1667bu4POqVR40k3d+OOSL3H/b7/j/cHxd//Ku+mcr93/Zmq770//mNrqRBIFgMVaWEbLGNeoapFacvMksQaIdxUrxmoslsPrPzzMk7leeOHF4PjsLE/GWU77py+b2aCZHV4y9mkz6zezp5s/vDGZEOKSYDlv478C4LbA+Ofd/drmz3fX1i0hxFpzwWB39x8B4InGQoifCVazQfcJMzvUfJu/mf2Tmd1lZn1m1ledHFvF6YQQq2Glwf5FAHsBXAtgAMBn2T+6+0F3P+DuB9pIdQ0hxPqzomB393PuvujudQBfAnDj2rolhFhrViS9mVmvuw80/3w/AN4H6DXzeLZRLAspT7LeClleg27oRe5StcrrzA2OUROeOfpscPz0BH/NzJUuo7Zsli//YCTj6cTZM9R2cmQyOD48yeWkX9rL6+QdOT1AbRP8kLi1vCc47lme3di7P5wpBwC7r/mn1HbsiR9yR5y3XWLMzkYy7CJZb5VyO7V1dFSorVQKr0l3N/10jDP94cdlYYHX47tgsJvZfQBuBdBjZqcB/B6AW83sWgAO4ASA377QcYQQG8sFg93dPxwYvmcdfBFCrCP6uqwQiaBgFyIRFOxCJIKCXYhEaGnWG2BUYou142ESVbXKJZLJV56ntlKGa0aZiFRz9nQ4662y7S10TmdPOAsNANpiRQg9XCgRADDLH7YzQ2FZ8btPHKVz6jV+n8eneRHF6TrPHBsZD0uA50Yn6JzBSX6f99zAs+VOH+eP9dTA8eB4rsLbWsXkqxibN3OpLPZYs+dxb28vnVNbCK/VmfM8y1JXdiESQcEuRCIo2IVIBAW7EImgYBciERTsQiRCi3u9GYr5cKaaRV52jBQHnBg5R+dMj56ltnKeZ9jV5rkMVe4KZydVuriMM1zlUtP5IS5dbW7nWVK1SFbWDPG/OhWWwgDg2RO8cKRFCiW+ZfcWahueDBf1PHKGS0MvnOBFNsfHeF+5zbu59Dk/SjIEIz3R5mKZY8UitcXktVrkmAu1cJHI6Wl+n2ERaZagK7sQiaBgFyIRFOxCJIKCXYhEULALkQgt3Y03M+RJUkukkxOKmfCcqbOv0DnzVd7iqS3Hd1Src7x9zhVbw/XkNnfzXdhTT/6U2sYy3I/y1bweWynyqI1Phe+317nKcOzkCWrbu3s7tXWXuSNPHw0nDR05xWvrvXgsnLQCAMMDXF25fHsXtVW2hZNJZof48eqRBJ/JSa5qlCt8x72jXKY2drZ6ne+4V6vhBCWPJFDpyi5EIijYhUgEBbsQiaBgFyIRFOxCJIKCXYhEWE5HmF0A/gzANjQ6wBx09y+YWTeA+wHsQaMrzAfdnWc5NI6FXI5Ib+DSUJ4kwoyf5lKNLfLj1SPyxELNqe2Fw88Ex3vOj9E5547yNlTTs7wW3q7NJWrr2LKD2nwhLMm05/nx5md4sk5PaSe1VSfGqK3vH54Ljh9/pZ/OGRrkba3mp7iPb9n/DmorXBauAXj81Mt8TpG3cdq6dSu1bdnCE4MW5rmkO3AmXDcwG2mJViiEk7JY0hiwvCt7DcDvuvt+AG8H8HEz2w/gbgCPuPs+AI80/xZCXKJcMNjdfcDdn2rengRwBMAOALcDuLf5b/cCuGOdfBRCrAEX9ZndzPYAuA7A4wC2LenkehaNt/lCiEuUZQe7mVUAPADgk+7+mg9Q7u5ofJ4PzbvLzPrMrG96fGRVzgohVs6ygt3M8mgE+tfc/cHm8Dkz623aewEMhua6+0F3P+DuB8qbutfCZyHECrhgsFtje+8eAEfc/XNLTN8B8NHm7Y8C+PbauyeEWCuWk/V2M4CPAHjGzJ5ujn0KwGcAfMPMPgbgJIAPLueE2WxYGshHioLVF+aD44MnX6BzLMtfx+oekTTyfEkmh8I10s6ePEnnjI6PUVttnktvTz/yLWrr7L2C2rw93IKoWOqic7ZezuW19tx+ajvxCpfKTp0Kr8nMGM96s/kxaivUufT2tl091DZdfltw/Pm+x+icbI7LtjHprRzJbBsY4P4Xi2EZbS5Sa7C9PSwPxtqoXTDY3f0x8Cy8X77QfCHEpYG+QSdEIijYhUgEBbsQiaBgFyIRFOxCJEJrC04CyIa/aIdCIdwWCgCqo8PB8cnR4Pd4GscjcgYAmHHpra3Ei0e2t4XljvHxcNYSAOQyXFKcirT3GR/hEtX4KP8mYrYQ9nEhw9c3+9brqe3smT3UVjO+xp3t4fONnuES1MgrR6htcZqvxytHn6K29//GB4LjLz/PC4E+/SQ/3qlTp6gtJnvVarwYZSEXfo5UVyC9rTbrTQjxc4CCXYhEULALkQgKdiESQcEuRCIo2IVIhNZKbxlDiRTKK+S5HDYyGZbeFua4dFUu8D5qiMgTMbq7w/n4nZ2ddE4u0sSu2M6Xf3yc9xSbmuKSTLYWXhOr89f1k88forbHOnkm1+59PCOuIx+WWOdHBoLjADBzPtwfDgDyGV4I9NEf/oDafu197wuOf/DO36RzhgfD2Y0AMDjI5d7h4fDzFABKJf58nCeFR/N5Lpe2tXGJmKEruxCJoGAXIhEU7EIkgoJdiERQsAuRCC3djc8Y0JYPv76Y87pfi7NTYUON70p7hidpxBIW6nXeGipL5nVs2kTnzEfa/szOhVs1AcDmzbwS79wCTwrxWvh8hRJv/9S1dTu1LURaEA0P85318XNng+NDZ3i9vlKO7z7H6rsNng2fCwD+4sEHguMf+kA4QQYA6pHkpaFhvhu/tYe3f9qzZw+1TU2GlRfWKg0A5ufDdRmVCCOEULALkQoKdiESQcEuRCIo2IVIBAW7EIlwQenNzHYB+DM0WjI7gIPu/gUz+zSA3wLwatbAp9z9u9FjAchaWNoq5CJta4ia0FnhyQBTkzPUFksiqNV4S6ZpUjOOJfcAwPbLe6ltdHSM2mYj9cc2VyrUNjIW9j/Tzu/zL/ziDdS2pZf7f+gnvIXSM31PBMdzdS4NtUWkpko5XHMNACoZLg8+9jfhJJkrr9hF5+x705uorcTVQezewY85v8CfVx3ljuD42NgYnTM8FK5DuLjIpePl6Ow1AL/r7k+ZWQeAJ83s4abt8+7+P5ZxDCHEBrOcXm8DAAaatyfN7AiAHevtmBBibbmoz+xmtgfAdQAebw59wswOmdmXzSzcPlQIcUmw7GA3swqABwB80t0nAHwRwF4A16Jx5f8smXeXmfWZWd/kGK93LoRYX5YV7GaWRyPQv+buDwKAu59z90V3rwP4EoAbQ3Pd/aC7H3D3Ax1d/PveQoj15YLBbo1v1t8D4Ii7f27J+NJt2vcDOLz27gkh1orl7MbfDOAjAJ4xs6ebY58C8GEzuxYNOe4EgN++0IEMQNHI6wsvMYaFhXDrnJ7Leuic0ZGXqC1W2yuWaVSthrPUqkVeX6zSEZZVAOCNb3wjtR06xOvCZSKZaKwt0CzJkgKA/hf46/TQy8eo7dhzvIVSFuEsxu5NvF5fZ5lLirF1rJFMPwCYnQtLsA9/76/onFtuvpnaet52DbVls7EsNd7+6Ux/OGsv1mpqYiLcRismHS9nN/4xNOL09UQ1dSHEpYW+QSdEIijYhUgEBbsQiaBgFyIRFOxCJEJrC05mDO1t4QyxRV5vEou1sGzR1s6LKGYjbZcWavxksWKULBNtnBQMBIBMRObbTNpJAcD+/by10pHnuFSWJ/e7OMclmYEXj1IbnGui5SzPYOvYFpZFd/by4pabI1+6skhm2/nz56htnhT1HB7kc44+9xy1Xf3mfdRW7uTSYWcH9//4SyeC47HMRyZHe+Tx0pVdiERQsAuRCAp2IRJBwS5EIijYhUgEBbsQidBS6a1BWALKGpcmfDE8px7R69oiBRanZ3iWVCEiXSyQjKLh0VE6Z45IJAAwEZHs9u69itr2X/1majt2LJylls/zopizc/w1P9aDr1Lh/dcuJ4Uqr7/uOjqnWIg8ZtO8gGixwOXNyfHwGsckqsFB3s+NSV4AcPnl26itrY0XzOzv7w+Ox6S3xZhWTdCVXYhEULALkQgKdiESQcEuRCIo2IVIBAW7EInQ2qw3AEWSvWQZnkEFUqSyxttaoaOTFzacnOQZT/U6PyjLiIvNGR8fpzZWwBIApqemqO3ADbzo4ehouDb/ufO8Zn+hwLMHuzq5ZLRjB89gK5fDslwuko1YifSwq9e5VLZrJ++xNj4WXv/h4WE6JybLxR7P+UhRz2KkKOkokW5X8lyMoSu7EImgYBciERTsQiSCgl2IRFCwC5EIF9yNN7MSgB8BKDb//5vu/ntmdiWArwO4DMCTAD7i7nw7snEslMhubC6yu5gju8Vzi3wHv9zGkypiG5mxBAO2SxtrGTU7y5NuYru31Rme+HHo0LPUtnPnzrAfkRp0c/Pcx927d1/0uQBgbGws7EeVn6tU5LaZyHqsZBe8VOIKROw5wFQGAMhG2nLNzfH71k1qEcaeV6zNU/YcVxmWc2WfA/Aud78GjfbMt5nZ2wH8IYDPu/sbAIwC+NgyjiWE2CAuGOze4FXRN9/8cQDvAvDN5vi9AO5YDweFEGvDcvuzZ5sdXAcBPAzgJQBj7v7qe4nTAHasi4dCiDVhWcHu7ovufi2AnQBuBMCrJ7wOM7vLzPrMrG98lH+eEEKsLxe1G+/uYwB+COAmAF1m9uoOwk4AwXIb7n7Q3Q+4+4FNmy9bja9CiFVwwWA3sy1m1tW83Qbg3QCOoBH0v9H8t48C+PY6+SiEWAOWkwjTC+BeM8ui8eLwDXf/SzN7DsDXzewPAPwDgHsudCADkCcvL8XIy05bMZyMMcnVDGyJHLC9jSclVCP16YCwJJPL8cSJGPPzvJ5ZLsfrqvX3n6e2Wi18vzs6eELLpk1cTtqxg8trsZpx+VxYKpub4/d5dHSM2kZGeCJPrD7dFEkoiiW7mHFJN5acshixzUaSnpgvhQKvG8iI+X7BYHf3QwD+UZVAdz+Oxud3IcTPAPoGnRCJoGAXIhEU7EIkgoJdiERQsAuRCBaTINb8ZGbnAZxs/tkDYKhlJ+fIj9ciP17Lz5ofu919S8jQ0mB/zYnN+tz9wIacXH7IjwT90Nt4IRJBwS5EImxksB/cwHMvRX68FvnxWn5u/Niwz+xCiNait/FCJIKCXYhE2JBgN7PbzOyomb1oZndvhA9NP06Y2TNm9rSZ9bXwvF82s0EzO7xkrNvMHjazY83fmzfIj0+bWX9zTZ42s/e2wI9dZvZDM3vOzJ41s3/XHG/pmkT8aOmamFnJzH5iZj9t+vH7zfErzezxZtzcb2YXlwPr7i39AZBFo4bdVQAKAH4KYH+r/Wj6cgJAzwac9x0ArgdweMnYHwG4u3n7bgB/uEF+fBrAv2/xevQCuL55uwPACwD2t3pNIn60dE3QKP1Qad7OA3gcwNsBfAPAnc3x/wXgdy7muBtxZb8RwIvuftwbdea/DuD2DfBjw3D3HwF4fTWG29Go0gu0qFov8aPluPuAuz/VvD2JRiWkHWjxmkT8aCneYM0rOm9EsO8AcGrJ3xtZmdYBPGRmT5rZXRvkw6tsc/eB5u2zALZtoC+fMLNDzbf56/5xYilmtgeNYimPYwPX5HV+AC1ek/Wo6Jz6Bt0t7n49gF8F8HEze8dGOwQ0XtnReCHaCL4IYC8aDUEGAHy2VSc2swqABwB80t0nltpauSYBP1q+Jr6Kis6MjQj2fgC7lvxNK9OuN+7e3/w9COBb2NgyW+fMrBcAmr8HN8IJdz/XfKLVAXwJLVoTM8ujEWBfc/cHm8MtX5OQHxu1Js1zj+EiKzozNiLYnwCwr7mzWABwJ4DvtNoJMyubWcertwG8B8Dh+Kx15TtoVOkFNrBa76vB1eT9aMGaWKNK4j0Ajrj755aYWromzI9Wr8m6VXRu1Q7j63Yb34vGTudLAP7TBvlwFRpKwE8BPNtKPwDch8bbwQU0Pnt9DI0GmY8AOAbg+wC6N8iPrwJ4BsAhNIKttwV+3ILGW/RDAJ5u/ry31WsS8aOlawLgbWhUbD6ExgvLf1nynP0JgBcB/DmA4sUcV1+XFSIRUt+gEyIZFOxCJIKCXYhEULALkQgKdiESQcEuRCIo2IVIhP8HySZy/9EnK/cAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "25552\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#从数据集中随机的选择10张图片进行显示\n",
    "import random\n",
    "for i in range(10):\n",
    "    a=random.choice(list(range(train_data.shape[0])))\n",
    "    print(a)\n",
    "    show_pic(cifatData[a],new_dict[train_target[a]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2634\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "33569\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "4084\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "20140\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "32639\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "32824\n"
     ]
    },
    {
     "data": {
      "image/png": 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QiSBnFyIR5OxCJIKcXYhEkLMLkQhdld5arQwWlsKBEBaRQmr1arA9H1FPFus8mKHZ5B29zqWLejMsG3kuIqtESvhYMyLjtLitkOPBJAOD4XxsmYjkZXkua01XuAQ4Ginl1Fsoh/flPAClSYJnAGChEj4HAMAbPGdclkhvrRY/ZrWIXJrLROSwSJ6/apXLpafPngm2z81xGXipFpadK/N8ntZS/ukLZnbBzB5f0fZxMztrZsc7P+9YbTtCiK1lLbfxXwTw9kD7p939cOfnexs7LCHERrOqs7v7fQCmujAWIcQmcjULdB82s0c7t/k0qbeZ3WFmx8zsWKPOv68JITaX9Tr7ZwEcBHAYwASAT7J/dPej7n7E3Y/k8jzriRBic1mXs7v7eXdvunsLwOcA3LaxwxJCbDTrkt7MbNzdJzp/vgvA47H/v0yz1UKF5Gvr7R+k/Wok19lchX8t6O8do7aRcS7xXHjhOWrz5XAet3xENszlItJbRDpsNPmh2THOc67t3rM/2H7qOf6+apF95bP8buxihee1a/aE89r1DnCZ79Isl5p8eYbaDu3jpaFoDkAPRwcCQN35MTt3YZrafvbkU9T28EMPU9vkpfCS2PBQmfYp9YfncX6eS3yrOruZfQXALwMYM7MzAH4fwC+b2WEADuAkgA+uth0hxNayqrO7+3sDzZ/fhLEIITYRPS4rRCLI2YVIBDm7EIkgZxciEboc9dZApRKWGUpD4QSFADAyGJbRzp0LJ68EAGR4RNbIdi7V9Ba5BHjmhZ8F2+vLPFnm2EhEAhzmtpbzQzM8fh21FUi5pmakVNPw2D5u28nHmOvh89jMh/fXMB5t5rEkm3Mz1DbQP0BtPX3h43lhkj8B/tBPf0ptP7r/fmqbnrxEbaVenkxz51h4jvfs4klCR8p9wfaeSOkqXdmFSAQ5uxCJIGcXIhHk7EIkgpxdiESQswuRCF2V3rzVRJNEL1krHCUFAEP924LtmR1cmnCSHBIAigWesDE2Jdt3haW+qfO8VtrCAo/Mm69MUFtPX5narrt5hNqq1fAYB4e20z4Hinw+alleT6+vzOc/XwhLTfXFGdpncvIcH8fcJLVNnL9Ibd/8zj3B9ieffIb2mZ/l+ypEkouODHD5eGyYR/vt2hmWMLcN88g8IHx+G/j4dGUXIhHk7EIkgpxdiESQswuRCHJ2IRKhq6vx8CZ8OZxnbGme5/aqLYdLGpXLPGjFnJcEqi/zgIvFCl99HhgoB9tLPTfRPvMVHiRzaYrnXOsf4AEo23YfoLalanj8i5EV694+HqTRqrWoDU2uoNQXw3O8VInkmYvsKt/DV7p/8vjPqa3ZCtti58Btt+6ltvlprhicOMHz/M1P82CdBRLkU9nJFZQhEjjWaPLzXld2IRJBzi5EIsjZhUgEObsQiSBnFyIR5OxCJMJaKsLsBfDnAHagXQHmqLt/xsxGAHwNwH60q8K82925ftbeGpx8vjQjkkFlbi7YXiT51gBgdKRMbRnjGs88yZEHAJfOhXOMteo8F55HSgnt238ztZVHeZBJZY4H1ywvVYPts3P80HgkeGJ87x5qqza4TDkzFZ6rWkSK7Mnz0zFX5EEhs5PnqS1LjvX1+7m8NlrmUuRAkUuioyNcCl5e5jJlLhfOG7dAZFQAqMyHJcBajfdZy5W9AeD33P0WAK8H8CEzuwXARwHc6+6HANzb+VsIcY2yqrO7+4S7P9J5XQHwFIDdAG4HcGfn3+4E8M5NGqMQYgO4ou/sZrYfwGsAPAhgx4pKrufQvs0XQlyjrPlxWTPrB3AXgI+4+5yZ/X+bu7tZOCG4md0B4A4AsGwsGF8IsZms6cpuZnm0Hf3L7v6tTvN5Mxvv2McBXAj1dfej7n7E3Y9kIoUbhBCby6rObu1L+OcBPOXun1phuhvA+zuv3w/gOxs/PCHERrGW2/g3AngfgMfM7Hin7WMAPgHg62b2AQCnALx71S1ZFtlCWJ5YmOfSRKMZjtiyyGfV4ACXQca2lantwA08oqwyHZZ4Jqd52Z++Ph6t1VjiMkne+F1QX4EftsZSWGrqi0hXI2M8uirXEy4zBABzMzPUhlZ4HEMlPo75qeDNIQBgeobLa/v3hHMUAsCRw7cG2193+NW0z8gY314+kq+v2eDy8eIil0t37doVbD/x8xO0z8+eejLYns1maZ9Vnd3d/w6AEfOvrNZfCHFtoCfohEgEObsQiSBnFyIR5OxCJIKcXYhE6GrCyVy+gLEd+4O2bJYt+APeCkdlVWbC0XAAcOb0i9SWzXNZa3AwnNwSAN74j94SbH/gh9+nfZp1HmHXU4zIJMbLVxVy/DO6VAy/t1fedAPtMzPDI+IeP/4AtWV6uIxmrXBCx9kKT3zJ+gDA637pELXdctP11PbKmw8G2w8d3E37lMpceltY4hGO1cVwxCEA9PXzSLpmK7zN8V38CfRyOZzA8n9+5Yu0j67sQiSCnF2IRJCzC5EIcnYhEkHOLkQiyNmFSISuSm/ZbB5Do+NB22i5RPvNkaiyyekZ2mcmIsvZKV6vKxYRd/MN+4Ltr7j1VbTPYz95mNoiAVSoViepbeIsl7y2j44E231pgfY58+yj1LanzPd18KbwfABAbTkc5bW0wKMA9+zeSW3bxrgkOtTfT207doYTS/ZFJNb+IR4xWeznkqhP8qi3ZpNLsK1WuN/gEH9fIyShar7AZWVd2YVIBDm7EIkgZxciEeTsQiSCnF2IROjqanwmC/SRBcbBIb7q2987HGwfLfPl7FyWr0o2SGANAKDOV63PT4RX8TPFcFACAHiOr6jOL/LV2z2jZWrbNsaVi0yzEmyfnjxN+9x6Q1ghAYCDN/AySYdewYNrLEvKfNUjAT6F9aUaz+X4aWxkdbpa4+PoaUVsfTygZSzLS0PNkRJmAC99FltZr1bDORvd+bmtK7sQiSBnFyIR5OxCJIKcXYhEkLMLkQhydiESYVXpzcz2AvhztEsyO4Cj7v4ZM/s4gN8GcDmp2Mfc/XuxbQ2Vivi1N4TzhW3fxmWLocGwfFUe5JJXf18vtU3O8lI8Tzw/QW1/e384qKUFLpGM7+HSVQ5cJumLlRlamqW24aFwuaYdN+2nfUqRXHjZPM8NuLDEc65t3xEuKVVf4nnmYrJRjMXIOBrNcImtAsnVBwC1Os8zF5PeeiPnXMt5IEy9Hh5jPpIr8dTpM+FtNbhsuBadvQHg99z9ETMbAPCwmd3TsX3a3f/7GrYhhNhi1lLrbQLAROd1xcyeAsBTcwohrkmu6Du7me0H8BoAD3aaPmxmj5rZF8ws/JibEOKaYM3Obmb9AO4C8BF3nwPwWQAHARxG+8r/SdLvDjM7ZmbHFhbCj3IKITafNTm7meXRdvQvu/u3AMDdz7t7091bAD4H4LZQX3c/6u5H3P1IqcQX1IQQm8uqzm5mBuDzAJ5y90+taF8ZPfEuAI9v/PCEEBvFWlbj3wjgfQAeM7PjnbaPAXivmR1GW447CeCDq22o1FPA6268LmibrXA56YVnHwu2T0dydO3cyUvnTFe4/LN3nJf+ObA7nN/tiaeeo316MlzWmp2aorZCKyyhAcDOIZ6rrchKQxmXtZZJ1BUAZDP8erC8FIkOK4RlqNEyX9pZXOSSaCMiKS0v8+PZaoUlr1ikXIzYvnp7ufQ2NMRz7zHpLSZFLi2RqDfyfoG1rcb/HYCQ2BrV1IUQ1xZ6gk6IRJCzC5EIcnYhEkHOLkQiyNmFSISuJpxcqjXwDCm9xKKTAKB3MCyHZYs8QWEzw2WQUj9/24MR222HwwkWf/Y4L/E0c4lLiiORqL2De7hEVZ25SG3nzzwfbO8vl/k4IqWVipGkh9kZ/kTki2fD0YM33hiOegSAcmSM1SqPbCtGIgSZxFbs4X1qNX4uskSaAJCJyJTtx1XCsESbTDYEgH27w+EpBZV/EkLI2YVIBDm7EIkgZxciEeTsQiSCnF2IROhyrTdD70A4Yd/YCI/kKhC5o77Eo6TyTZ40sFTiSQOHhrktg3CU3d7xMu2za4zLa7fefBO13XBgH7WhzqOhnnzm2WD7iRdfpH1mKrwO2UA/H39zapraqvPhbRYKXIJ65a2vpLZMide3azZ5RFyGRR3yYUTlq2wm0jFCLGoPRJZrRaIRm82wLBfL2akruxCJIGcXIhHk7EIkgpxdiESQswuRCHJ2IRKhq9JbNpPB0GBY2lpe4vLPYi2c5C8fiUDqKfGEjT09XFpZJon8AGCIyD+/9Zvvon3+93fuorbZS09TW+Egj9ob38dluRtufFOwfarKZcqfnz5NbbNTPMKuvsij3rIePp61mSdon4sv8O0N9A9SW0+Rz1WxED7f8nke9YYsl1+b4Puq17gs13R+ziEflgcvTZ+nXR45/lCwvVpdoH10ZRciEeTsQiSCnF2IRJCzC5EIcnYhEmHV1Xgz6wFwH4Bi5/+/6e6/b2YHAHwVwCiAhwG8z9159AmAlrewtBheWfcWf+i/TkrulPr4irtnedmlxRofZixXWKkYtmUbPM/cUGGe2pYq4Xx8ADB5hudBy1VPUFuxhwSMRFafdxV5kMaObXwVP9PiykWrFT5m9RZXXabPnaG2xciKe18vD5Lp6w0HL42O8DJfWOZlxVpZXsbJszyXXybDy5FNngsHFP3NfT+kfR47/pNgeyxX31qu7MsA3uLur0a7PPPbzez1AP4IwKfd/QYA0wA+sIZtCSG2iFWd3dtcvjzlOz8O4C0AvtlpvxPAOzdjgEKIjWGt9dmznQquFwDcA+B5ADPufvn+7wyAcG5bIcQ1wZqc3d2b7n4YwB4AtwG4ea07MLM7zOyYmR2rzPHva0KIzeWKVuPdfQbADwC8AUDZzC4v8O0BcJb0OeruR9z9yMAgf+RRCLG5rOrsZrbNzMqd170A3grgKbSd/p93/u39AL6zSWMUQmwAawmEGQdwp5ll0f5w+Lq7/6WZPQngq2b2hwB+AuDzq22o2WhibiYsU42OcEmj3B+WNGI5uqzFk3FljX/GDQxyiefC6XB+t+MP3EP7LFx6ktoKGS5rnXySy1AvGH9vw+Vw2aiBQT6/Fsmr1t/PZa2RYb7NvgEii+a5rJXN8tOxkOelvuKllcLtpd6wNAgAOeNll2oNLm0hyyXdmfMXqO2H338w2P6TR3mgFEhuPY+UjFrV2d39UQCvCbSfQPv7uxDi7wF6gk6IRJCzC5EIcnYhEkHOLkQiyNmFSATzWL2Yjd6Z2UUApzp/jgG41LWdczSOl6JxvJS/b+O4zt2DIX1ddfaX7NjsmLsf2ZKdaxwaR4Lj0G28EIkgZxciEbbS2Y9u4b5XonG8FI3jpfzCjGPLvrMLIbqLbuOFSAQ5uxCJsCXObmZvN7Onzew5M/voVoyhM46TZvaYmR03s2Nd3O8XzOyCmT2+om3EzO4xs2c7v8Oxqps/jo+b2dnOnBw3s3d0YRx7zewHZvakmT1hZr/bae/qnETG0dU5MbMeM/uxmf20M44/6LQfMLMHO37zNTPjcb8h3L2rPwCyaOewux5AAcBPAdzS7XF0xnISwNgW7PfNAF4L4PEVbX8M4KOd1x8F8EdbNI6PA/h3XZ6PcQCv7bweAPAMgFu6PSeRcXR1TgAYgP7O6zyABwG8HsDXAbyn0/6nAH7nSra7FVf22wA85+4nvJ1n/qsAbt+CcWwZ7n4fgKmXNd+OdpZeoEvZesk4uo67T7j7I53XFbQzIe1Gl+ckMo6u4m02PKPzVjj7bgArawRvZWZaB/BXZvawmd2xRWO4zA53n+i8PgeAVxXYfD5sZo92bvM3/evESsxsP9rJUh7EFs7Jy8YBdHlONiOjc+oLdG9y99cC+FUAHzKzN2/1gID2JzvaH0RbwWcBHES7IMgEgE92a8dm1g/gLgAfcX9pgfduzklgHF2fE7+KjM6MrXD2swD2rvibZqbdbNz9bOf3BQDfxtam2TpvZuMA0PnNk5ZtIu5+vnOitQB8Dl2aEzPLo+1gX3b3b3Wauz4noXFs1Zx09j2DK8zozNgKZ38IwKHOymIBwHsA3N3tQZhZycwGLr8G8DYAj8d7bSp3o52lF9jCbL2XnavDu9CFObF2xsjPA3jK3T+1wtTVOWHj6PacbFpG526tML5stfEdaK90Pg/gP27RGK5HWwn4KYAnujkOAF9B+3awjvZ3rw+gXSDzXgDPAvhrACNbNI4vAXgMwKNoO9t4F8bxJrRv0R8FcLzz845uz0lkHF2dEwCvQjtj86Nof7D85xXn7I8BPAfgGwCKV7JdPS4rRCKkvkAnRDLI2YVIBDm7EIkgZxciEeTsQiSCnF2IRJCzC5EI/w/PJVISQa79GAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "7111\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "19560\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "20937\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "28477\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import random\n",
    "for i in range(10):\n",
    "    a=random.choice(list(range(train_data.shape[0])))\n",
    "    print(a)\n",
    "    show_pic(cifatData[a],new_dict[train_target[a]])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 反正就是个10分类的问题吧  根据已有的数据集 训练 然后预测吧！"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<torch.utils.data.dataloader.DataLoader object at 0x7f8b0220eb70>\n"
     ]
    }
   ],
   "source": [
    "trainloader = torch.utils.data.DataLoader(trainset, batch_size=BATCH_SIZE, shuffle=True)#这里的4个参数都有对应的意思，shuffle表示打乱\n",
    "print(trainloader)#我们这里批次batch_size设置的是32 "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "torch.utils.data.dataloader.DataLoader"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(trainloader)#这个trainloader有哪些性质呢 看看吧"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Files already downloaded and verified\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Dataset CIFAR10\n",
       "    Number of datapoints: 10000\n",
       "    Split: test\n",
       "    Root Location: ./\n",
       "    Transforms (if any): Compose(\n",
       "                             Resize(size=(32, 32), interpolation=PIL.Image.BILINEAR)\n",
       "                             ToTensor()\n",
       "                             Normalize(mean=[0.4914, 0.4822, 0.4465], std=[0.247, 0.243, 0.261])\n",
       "                         )\n",
       "    Target Transforms (if any): None"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#这里是拿到测试集 测试集相对于训练集  有一个参数改了  就是train=False 这里  就下载的是数据集\n",
    "testset = torchvision.datasets.CIFAR10(root='./', train=False, download=True, transform=transform)\n",
    "testset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['airplane', 'automobile', 'bird', 'cat', 'deer', 'dog', 'frog', 'horse', 'ship', 'truck'] {'airplane': 0, 'automobile': 1, 'bird': 2, 'cat': 3, 'deer': 4, 'dog': 5, 'frog': 6, 'horse': 7, 'ship': 8, 'truck': 9} (10000, 32, 32, 3) [3, 8, 8, 0, 6, 6, 1, 6, 3, 1, 0, 9, 5, 7, 9, 8, 5, 7, 8, 6, 7, 0, 4, 9, 5, 2, 4, 0, 9, 6, 6, 5, 4, 5, 9, 2, 4, 1, 9, 5, 4, 6, 5, 6, 0, 9, 3, 9, 7, 6, 9, 8, 0, 3, 8, 8, 7, 7, 4, 6, 7, 3, 6, 3, 6, 2, 1, 2, 3, 7, 2, 6, 8, 8, 0, 2, 9, 3, 3, 8, 8, 1, 1, 7, 2, 5, 2, 7, 8, 9, 0, 3, 8, 6, 4, 6, 6, 0, 0, 7, 4, 5, 6, 3, 1, 1, 3, 6, 8, 7, 4, 0, 6, 2, 1, 3, 0, 4, 2, 7, 8, 3, 1, 2, 8, 0, 8, 3, 5, 2, 4, 1, 8, 9, 1, 2, 9, 7, 2, 9, 6, 5, 6, 3, 8, 7, 6, 2, 5, 2, 8, 9, 6, 0, 0, 5, 2, 9, 5, 4, 2, 1, 6, 6, 8, 4, 8, 4, 5, 0, 9, 9, 9, 8, 9, 9, 3, 7, 5, 0, 0, 5, 2, 2, 3, 8, 6, 3, 4, 0, 5, 8, 0, 1, 7, 2, 8, 8, 7, 8, 5, 1, 8, 7, 1, 3, 0, 5, 7, 9, 7, 4, 5, 9, 8, 0, 7, 9, 8, 2, 7, 6, 9, 4, 3, 9, 6, 4, 7, 6, 5, 1, 5, 8, 8, 0, 4, 0, 5, 5, 1, 1, 8, 9, 0, 3, 1, 9, 2, 2, 5, 3, 9, 9, 4, 0, 3, 0, 0, 9, 8, 1, 5, 7, 0, 8, 2, 4, 7, 0, 2, 3, 6, 3, 8, 5, 0, 3, 4, 3, 9, 0, 6, 1, 0, 9, 1, 0, 7, 9, 1, 2, 6, 9, 3, 4, 6, 0, 0, 6, 6, 6, 3, 2, 6, 1, 8, 2, 1, 6, 8, 6, 8, 0, 4, 0, 7, 7, 5, 5, 3, 5, 2, 3, 4, 1, 7, 5, 4, 6, 1, 9, 3, 6, 6, 9, 3, 8, 0, 7, 2, 6, 2, 5, 8, 5, 4, 6, 8, 9, 9, 1, 0, 2, 2, 7, 3, 2, 8, 0, 9, 5, 8, 1, 9, 4, 1, 3, 8, 1, 4, 7, 9, 4, 2, 7, 0, 7, 0, 6, 6, 9, 0, 9, 2, 8, 7, 2, 2, 5, 1, 2, 6, 2, 9, 6, 2, 3, 0, 3, 9, 8, 7, 8, 8, 4, 0, 1, 8, 2, 7, 9, 3, 6, 1, 9, 0, 7, 3, 7, 4, 5, 0, 0, 2, 9, 3, 4, 0, 6, 2, 5, 3, 7, 3, 7, 2, 5, 3, 1, 1, 4, 9, 9, 5, 7, 5, 0, 2, 2, 2, 9, 7, 3, 9, 4, 3, 5, 4, 6, 5, 6, 1, 4, 3, 4, 4, 3, 7, 8, 3, 7, 8, 0, 5, 7, 6, 0, 5, 4, 8, 6, 8, 5, 5, 9, 9, 9, 5, 0, 1, 0, 8, 1, 1, 8, 0, 2, 2, 0, 4, 6, 5, 4, 9, 4, 7, 9, 9, 4, 5, 6, 6, 1, 5, 3, 8, 9, 5, 8, 5, 7, 0, 7, 0, 5, 0, 0, 4, 6, 9, 0, 9, 5, 6, 6, 6, 2, 9, 0, 1, 7, 6, 7, 5, 9, 1, 6, 2, 5, 5, 5, 8, 5, 9, 4, 6, 4, 3, 2, 0, 7, 6, 2, 2, 3, 9, 7, 9, 2, 6, 7, 1, 3, 6, 6, 8, 9, 7, 5, 4, 0, 8, 4, 0, 9, 3, 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1, 4, 0, 5, 1, 6, 3, 0, 2, 5, 5, 5, 2, 8, 2, 3, 1, 6, 2, 7, 0, 8, 9, 2, 6, 4, 7, 5, 5, 8, 8, 7, 8, 5, 7, 4, 4, 0, 0, 7, 0, 4, 0, 4, 4, 2, 5, 4, 0, 7, 7, 7, 0, 4, 6, 2, 9, 1, 1, 5, 0, 9, 1, 0, 6, 5, 3, 0, 9, 0, 6, 2, 0, 4, 1, 6, 7, 6, 1, 0, 4, 6, 0, 4, 4, 2, 7, 6, 5, 3, 5, 9, 4, 5, 2, 0, 5, 7, 2, 3, 9, 4, 5, 8, 1, 9, 3, 1, 8, 5, 8, 6, 6, 3, 4, 0, 2, 3, 2, 6, 1, 8, 2, 1, 3, 6, 2, 2, 3, 3, 5, 0, 2, 1, 2, 8, 0, 8, 2, 2, 7, 3, 5, 9, 8, 3, 6, 1, 0, 8, 6, 3, 5, 8, 9, 4, 8, 0, 0, 2, 9, 1, 8, 3, 7, 2, 8, 0, 9, 4, 8, 9, 4, 8, 9, 5, 4, 8, 1, 5, 5, 7, 2, 5, 8, 8, 1, 2, 3, 1, 5, 0, 2, 3, 0, 8, 2, 2, 8, 7, 3, 5, 3, 9, 6, 3, 5, 1, 1, 7, 7, 3, 3, 8, 9, 6, 0, 8, 0, 6, 6, 4, 6, 9, 1, 1, 7, 0, 6, 8, 9, 0, 6, 9, 0, 1, 1, 7, 2, 1, 0, 4, 5, 0, 3, 6, 3, 3, 8, 1, 8, 1, 3, 1, 5, 4, 5, 1, 8, 9, 1, 3, 1, 7, 2, 9, 8, 9, 4, 6, 0, 9, 4, 0, 4, 6, 2, 1, 3, 0, 8, 5, 6, 0, 3, 4, 6, 9, 5, 7, 9, 0, 3, 6, 5, 7, 0, 5, 1, 2, 2, 9, 7, 0, 0, 1, 4, 4, 5, 5, 9, 6, 3, 6, 2, 6, 9, 3, 2, 3, 2, 4, 9, 5, 2, 1, 6, 6, 4, 7, 6, 3, 3, 8, 1, 4, 6, 4, 4, 5, 6, 2, 0, 5, 2, 7, 5, 2, 5, 2, 5, 9, 0, 6, 7, 9, 8, 3, 2, 1, 4, 3, 4, 5, 5, 6, 8, 6, 0, 8, 9, 8, 0, 7, 7, 6, 6, 7, 5, 9, 8, 7, 9, 5, 6, 0, 4, 8, 2, 0, 9, 1, 3, 9, 8, 3, 7, 4, 5, 0, 2, 1, 9, 1, 0, 5, 8, 4, 7, 3, 1, 4, 5, 3, 1, 0, 8, 8, 8, 2, 1, 3, 7, 3, 1, 6, 9, 1, 6, 0, 7, 7, 1, 3, 3, 3, 1, 0, 2, 7, 1, 2, 2, 9, 6, 3, 5, 9, 4, 8, 0, 4, 8, 0, 1, 3, 7, 0, 4, 7, 4, 8, 6, 4, 3, 9, 8, 2, 2, 8, 3, 1, 1, 2, 8, 2, 6, 9, 4, 8, 4, 4, 1, 5, 2, 6, 9, 2, 0, 7, 1, 8, 9, 3, 9, 9, 0, 7, 7, 5, 4, 2, 6, 4, 5, 7, 7, 8, 7, 2, 6, 2, 2, 4, 4, 0, 7, 1, 3, 9, 6, 0, 0, 2, 3, 8, 2, 2, 4, 3, 5, 2, 9, 1, 0, 0, 6, 5, 5, 7, 9, 9, 6, 5, 5, 0, 5, 7, 1, 6, 6, 4, 1, 4, 4, 1, 5, 0, 0, 4, 5, 8, 4, 8, 3, 0, 5, 0, 5, 3, 1, 6, 7, 0, 9, 1, 5, 7, 6, 5, 5, 5, 6, 0, 0, 1, 7, 5, 1, 9, 2, 4, 1, 3, 7, 8, 2, 0, 9, 6, 6, 0, 6, 5, 8, 2, 7, 4, 0, 2, 7, 7, 8, 8, 7, 0, 4, 9, 1, 4, 4, 3, 5, 4, 6, 2, 3, 1, 0, 3, 3, 3, 6, 3, 1, 2, 8, 9, 7, 9, 3, 8, 7, 3, 1, 7, 7, 3, 2, 2, 8, 9, 5, 9, 2, 1, 7, 4, 4, 0, 5, 7, 1, 5, 4, 0, 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1, 3, 9, 0, 9, 3, 6, 7, 2, 7, 3, 0, 5, 9, 7, 5, 5, 0, 6, 5, 1, 8, 2, 7, 5, 9, 0, 0, 0, 8, 8, 7, 3, 7, 8, 9, 3, 7, 9, 7, 8, 7, 9, 8, 5, 4, 8, 3, 7, 6, 3, 8, 2, 1, 9, 5, 7, 3, 9, 5, 5, 8, 7, 3, 5, 3, 5, 9, 7, 6, 7, 3, 6, 4, 3, 9, 4, 2, 1, 9, 6, 0, 2, 6, 7, 4, 7, 9, 0, 7, 4, 3, 5, 3, 1, 1, 2, 6, 8, 2, 1, 7, 8, 5, 9, 6, 1, 1, 5, 0, 6, 0, 9, 2, 6, 5, 8, 9, 5, 5, 6, 2, 9, 1, 5, 8, 8, 7, 1, 7, 3, 5, 4, 9, 7, 5, 2, 9, 9, 4, 7, 4, 1, 3, 8, 7, 9, 0, 4, 5, 7, 5, 2, 8, 7, 6, 9, 6, 9, 3, 8, 5, 6, 6, 9, 5, 7, 8, 0, 5, 0, 7, 4, 8, 2, 5, 1, 3, 2, 2, 6, 2, 1, 7, 4, 6, 3, 1, 3, 7, 2, 1, 3, 7, 0, 8, 4, 4, 5, 7, 9, 5, 4, 3, 9, 6, 8, 2, 3, 3, 1, 6, 1, 7, 0, 3, 4, 2, 9, 4, 5, 8, 2, 7, 0, 9, 6, 8, 0, 8, 2, 8, 5, 7, 7, 2, 2, 0, 0, 0, 7, 4, 1, 6, 6, 8, 8, 9, 0, 9, 0, 1, 3, 3, 0, 9, 6, 6, 2, 6, 3, 4, 0, 8, 4, 1, 4, 0, 6, 5, 0, 9, 9, 9, 9, 1, 2, 3, 5, 4, 2, 9, 6, 0, 9, 6, 6, 8, 0, 6, 1, 4, 6, 8, 0, 5, 4, 1, 2, 0, 9, 6, 4, 2, 4, 6, 5, 9, 7, 7, 4, 6, 5, 0, 1, 9, 0, 3, 1, 9, 0, 9, 7, 8, 6, 7, 6, 8, 2, 4, 5, 3, 0, 3, 2, 1, 7, 5, 9, 3, 4, 5, 7, 1, 5, 0, 1, 1, 1, 9, 7, 5, 4, 9, 7, 8, 1, 0, 2, 8, 5, 6, 7, 0, 1, 4, 8, 4, 4, 6, 6, 5, 8, 1, 8, 4, 6, 5, 9, 2, 2, 1, 4, 9, 1, 6, 7, 2, 0, 1, 7, 6, 5, 2, 2, 5, 6, 0, 9, 0, 1, 5, 3, 3, 5, 8, 7, 5, 6, 5, 8, 0, 5, 9, 4, 6, 5, 1, 1, 0, 3, 3, 9, 4, 8, 1, 7, 7, 9, 9, 4, 3, 6, 3, 2, 8, 2, 7, 6, 7, 0, 2, 1, 2, 9, 4, 6, 9, 6, 1, 0, 1, 8, 7, 0, 0, 4, 7, 4, 2, 6, 9, 5, 9, 0, 7, 4, 5, 8, 1, 4, 7, 9, 9, 8, 8, 6, 3, 7, 0, 8, 9, 6, 2, 4, 6, 2, 9, 7, 4, 6, 8, 5, 6, 1, 3, 5, 9, 9, 1, 3, 2, 0, 3, 0, 2, 0, 7, 3, 9, 3, 5, 7, 6, 5, 9, 5, 6, 1, 2, 4, 0, 2, 7, 8, 4, 4, 0, 9, 5, 9, 2, 9, 3, 2, 4, 3, 2, 2, 8, 8, 6, 8, 1, 6, 8, 9, 8, 2, 1, 4, 9, 1, 5, 7, 1, 6, 0, 1, 5, 2, 8, 1, 1, 3, 8, 1, 3, 1, 8, 5, 1, 9, 0, 3, 0, 4, 0, 5, 1, 2, 0, 4, 9, 5, 1, 2, 2, 7, 8, 7, 2, 4, 5, 3, 4, 0, 6, 1, 5, 9, 8, 0, 2, 0, 7, 6, 0, 5, 9, 7, 5, 0, 4, 6, 0, 6, 3, 6, 8, 1, 1, 8, 9, 7, 2, 9, 0, 2, 2, 9, 0, 6, 5, 7, 7, 9, 1, 7, 9, 8, 4, 5, 0, 8, 0, 2, 5, 2, 1, 4, 4, 8, 9, 7, 8, 3, 6, 6, 0, 1, 1, 1, 8, 1, 4, 4, 0, 7, 8, 2, 1, 2, 5, 4, 6, 0, 5, 7, 4, 4, 3, 9, 5, 8, 8, 0, 8, 7, 4, 1, 8, 4, 9, 5, 4, 1, 7, 7, 7, 7, 0, 3, 8, 3, 3, 0, 5, 7, 0, 8, 0, 0, 9, 2, 2, 3, 4, 8, 2, 2, 6, 3, 3, 6, 2, 9, 4, 0, 1, 7, 5, 5, 7, 3, 0, 4, 2, 0, 7, 5, 8, 0, 8, 2, 7, 0, 3, 5, 3, 8, 3, 5, 1, 7]\n"
     ]
    }
   ],
   "source": [
    "print(testset.classes,testset.class_to_idx,testset.data.shape,testset.targets)\n",
    "#也可以用同样的方法来进行一下随机选择10张图片显示"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "7750\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1703\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "182\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "9724\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1910\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "6386\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "6653\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2142\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "9011\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "62\n"
     ]
    },
    {
     "data": {
      "image/png": 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+CniM589z6erYiWepb103f866SmEpNV/i23yNVXhl3kikWq5/pJ/6Lg9eDNorl7hsaEM8jomRSCVajff5c+fX1anp8PmqRT4m003OxZXN+ZPdzL4H4GMA1pvZeQBfAvAxM7sDgAM4A+AP5juOEKK1zJvs7v65gPk7KxCLEGIF0ddlhUgEJbsQiaBkFyIRlOxCJEJzq96qjvJ4WBrKZbmMVh4LS1Sza/l7VbGDS01r27lE8sYb56hvMNsTtO/ZwLdI6jAeY087l6FsI29sODMQkY1Gw/Pb0c6bSvY6l9B8jEuYa7o7qa9AirzORLZBujLFH9cV8Iq4KeMVgtkd4YrENXnedDQ3wSXRLbfuo74yabIJANNjXDqcGRkO2kenRugYsKkf5ufRlV2IRFCyC5EISnYhEkHJLkQiKNmFSAQluxCJ0FzprVZDeSxc4XMFvJJrTU9YCtmU47LQzq280ePI+DD1lUpcepuYCo87f/EdOubeD9xMfb1dXLpak+dSZKmT9grB5cGjQfvApTN0THsnl6E+cAuvXu7o5hV97w68EbSfffc8HWOR57N3A5cipyJtEq6SKrvRIS5rlc/xKrqZXi6Xrv3gLdRXKnB5tjQdbviZH+bNLafJmMppXsGoK7sQiaBkFyIRlOxCJIKSXYhEULILkQhNXY23ag1tY+FVRGvnfb/GZsJFEFbg2y4V2viqaSGybdSaTZupbxbhApqjrx2lYz7yG3dQ320lvlI/MToc8XHloqcUXsXft3cXHXN5nK9MnzpzhvpOHjtFfe+cOxa0T00M0TEzk7wQZvxclvq23bSf+rp29AXtb2ziWyFkRngcQ1d4/8LyRf666ty4gfpK+XAaFjq4WoNSWEGxHFeGdGUXIhGU7EIkgpJdiERQsguRCEp2IRJByS5EIixkR5jtAP4cQB/qO8AcdvdvmlkvgL8CsAv1XWF+z92vxo5VnZjCyAuvBH1r9oe3CwKAnlJP0D4+zE93Yobvg9PV3Ut9m/u49Hb81bCcdOa1V+mYJ59cT329PRGpZk0P9VV4yzX82s17gvb2yKaar791gvrOngsXtABA/9AF6st6eP7LV4fpmPFLl6lvdpI/6LOnuRy27uCBoD1f5D3tbMc66it09lBfvp2/hrMTkd5w2XB/wJkq74U3WQ5L1bUqaf6HhV3ZKwD+yN0PALgXwB+a2QEADwN42t33A3i68X8hxCpl3mR39353f6nx+xiAkwC2ArgfwKONP3sUwKdXKEYhxDJwQ5/ZzWwXgDsBPAegb85OrhdRv80XQqxSFpzsZtYJ4AcAvuDu132f0N0d9c/zoXGHzOyImR3xGv88IYRYWRaU7GaWRz3Rv+vuP2yYL5nZ5oZ/M4BgWw13P+zuB939oGW0+C9Eq5g3+8zMUN+i+aS7f32O68cAHmj8/gCAx5c/PCHEcrGQqrcPA/g8gFfM7GjD9kUAXwHwfTN7EMBZAL8334GymQy6imF5ov8tLvH0rd8StJ+u8mqtjet5ldHa6Qr1VUfHqO/sL48E7ZnpCTrmlRf+ifoKBS7V3HTgdurziCTz4YN3B+1Dg1z6efpnP6W+d86/RX3I8UrF8UvhPm6T73J5LTfFP+a58aq38gSvArxwPLzdVNudvGLSa/x4mcjctxW4vOnGtxyb9nDvvWyVX4u7CuGqt4nI3fO8ye7uvwBIbSfwm/ONF0KsDvQhWohEULILkQhKdiESQckuRCIo2YVIhOZu/+SOWi0se00O8gaAb595LWjPr+fbIF0Z5U0DMcolu+H+M9TXkSVSE1eFMDvNz/XSi89SX38/38anu5d/M3m6Gm7oeXngEh1z6vXXqW/yykXqmxjj1WYzo2GpL8dVT2QjE1mlglBc1qqNhqvbpl7lsXft5rLcmi5eEefO46hEvj06S+LvyvOmqZXwF1ZhkbnQlV2IRFCyC5EISnYhEkHJLkQiKNmFSAQluxCJ0FzpDY5pD1cNZcJFPACAkexw0L6uhzeHHLzMGwrmZrj+Uynw/bXK7eFxuSKvkuoie68BwJYNvDKvc20X9eXWcEnm2ef/IWh3UlkFAJOTXB68eOYs9ZEtygAAGSKj1SIyZc25PFWNSFf8kQFtpE9l9dwwHTMyyZ/PO+/7JPVN5Xgkp375IvXlcuFJmXL+Op2shfMoNk+6sguRCEp2IRJByS5EIijZhUgEJbsQidDU1fh8Po+tG8NFBiffDW+tBADZ6XCRzPgML9LId/L+btMeLhYBgK6dvMhkc/e+oL1a41sTdZV5z7L1W7ZSX7nE41/TywuA9q0Nr/6fu8jnqu/ALurLkeMBwMQY72sHprqArxaXpyJbJIHP42yVr1rPkgIatuUSABTzXDIYGuI99Lr37aS+TIWv1F89H95Ga9OBW/i51nYH7WOFkzwG6hFC/EqhZBciEZTsQiSCkl2IRFCyC5EISnYhEmFe6c3MtgP4c9S3ZHYAh939m2b2ZQD/BsC1Zl5fdPcnYsfKAGjLhGWNdl57gJmzYdloJsf7bXVt40Uy7R38PW68wiWq6anw1lBe4PKUF/kUl6eGqK+71Et9XTVeJNPZ3RO0l7J8rrCZF+Tkd3I5bHqUb3s1Pn41aK9VuOzZE3k+20u8UmqWq2gYLYefs0qVv+Am3z1Pfa+8+Az1rX93O/XdtudW6hvtDW9v1rWbS7Mda3uC9ncKPL6F6OwVAH/k7i+ZWReAF83sqYbvG+7+PxZwDCFEi1nIXm/9APobv4+Z2UkA/C1HCLEquaHP7Ga2C8CdAJ5rmB4ys2Nm9oiZ8a91CSFazoKT3cw6AfwAwBfcfRTAtwDsBXAH6lf+r5Fxh8zsiJkdmZ2NNA0XQqwoC0p2M8ujnujfdfcfAoC7X3L3qrvXAHwbwD2hse5+2N0PuvvBfKy1iRBiRZk32a2+xcR3AJx096/Psc9d7v4MgOPLH54QYrlYyKX2wwA+D+AVMzvasH0RwOfM7A7U5bgzAP5gvgNVqjUMkUqpyM45qI6Hb/+zXMVBZweXrja1l6ivENk+p0y2rpqN9HebnuUVcYNjfMuroSm+fdXE1bCcBAC9PWEZrdLO+9aNGI+/Eql6W7eez3FpKryEMxx5zNMROayyhj9nsxXeb/Dy628E7bWrA3RMdTQsGwLAbJXP1cZ9N1Pf/h27qa+ypxC0D0WqM9mrNJJGC1qN/wU5RlRTF0KsLvQNOiESQckuRCIo2YVIBCW7EImgZBciEZr6LZfK7CwGzoeryjzD33eYGpYdm6Fjrj5/gvpmeO9CdBa4RDVL4pio8ThQ4Sfr2LCO+jbd9QHqu9LNy7wquXAs3bO80eP46CD1jWV5/KPOn7MKkRxrBR57pptX801H5nhimFcPzoyFZbn8FJdEc7yYD9mONdS3ZvtN1DeZ44+7Wp0Onysy9+2ZcOpmItqbruxCJIKSXYhEULILkQhKdiESQckuRCIo2YVIhKZKb2ZAO2kqOAVeTZSthcdUL/J9t9DNq7WGIr4rM7zSKE9knGyZN+WoRareJvt5c8tRsv8XAHTcHd5zDgAmd24L2st9YTsAZHu55FWdHKG+q5EqNcyEK/OyE2GZCQDKV/ncT4/zc+Ujsm3GwpJjOcfHVLPct7YvvFchAOTWdlLfsPHXgZOcyMfS05iUx7U3XdmFSAQluxCJoGQXIhGU7EIkgpJdiERQsguRCE3v7ezZsMSWieyJlquEx0xUueRV2s4lkq6DfN8tgFcarRkPS2+1aS6rlMvcV5ngjRInBnglWvmlV6iveups0H7ltgN0zMbbuJRXKHIpZ/IClw4nLrwTtM+Ocrm0OsPnA7xoD5lcTKIiAyNNQlHk18Bajo97+82XqS+LcFNJACiuCzcJbW/nEnGuHI4j1uBUV3YhEkHJLkQiKNmFSAQluxCJoGQXIhHmXY03szYAzwIoNv7+r939S2a2G8BjANYBeBHA5909UhkB5At59O3YFPR96Nc/TMddvRjejucfX/gnfrJSO3UVsnyFuZrlhyz2ho856XzVNBPbTup8P/X5EF/1rQ3zFdfqu+FV/NGBF+iY6bff4ufq4fHPTA9TH9iqcI2rHfl8G/UV8ryHWzZSuMIW8cszfA5rHunXN8gLlEYvvk19AH9hdezaH7R7ls9HeSDcd68yxguXFnJlnwHwCXf/IOrbM99nZvcC+CqAb7j7PgBXATy4gGMJIVrEvMnuda7txphv/HMAnwDw1w37owA+vRIBCiGWh4Xuz55t7OA6AOApAG8BGHb3a99qOQ9g64pEKIRYFhaU7O5edfc7AGwDcA+AWxZ6AjM7ZGZHzOxIJdJDXQixstzQary7DwN4BsCHAPSY2bUFvm0AgisX7n7Y3Q+6+8FcLrL6JYRYUeZNdjPbYGY9jd/bAfw2gJOoJ/2/bPzZAwAeX6EYhRDLwEIKYTYDeNTMsqi/OXzf3X9qZq8CeMzM/gTALwF8Z74DZYsFdO8J90Lr27uXjtt9a1h2GerihTADU+PUNzwQlvIAoFrkdx+T5fAWRG2RbZDaOvl2QW2z/FznL4d7uAFApRyRr8hTmhvmRSaVY3yuZrmqiOwGvlXWmp19QXthQy8dUypwubQY8SHSv7BMpL6xQV6QM/baGeqrXOXzmCtwmXK2MyKlXr4StP/aNr4F2N67bw/a//Y4v+bOm+zufgzAnQH7adQ/vwsh3gfoG3RCJIKSXYhEULILkQhKdiESQckuRCKYx3pxLffJzAYBXGuSth5AZP+mpqE4rkdxXM/7LY6d7h5satfUZL/uxGZH3P1gS06uOBRHgnHoNl6IRFCyC5EIrUz2wy0891wUx/Uojuv5lYmjZZ/ZhRDNRbfxQiSCkl2IRGhJspvZfWb2upm9aWYPtyKGRhxnzOwVMztqZkeaeN5HzGzAzI7PsfWa2VNmdqrxc22L4viymV1ozMlRM/tUE+LYbmbPmNmrZnbCzP5Dw97UOYnE0dQ5MbM2M3vezF5uxPFfGvbdZvZcI2/+ysz4BnIh3L2p/1DvqfsWgD0ACgBeBnCg2XE0YjkDYH0LzvtRAHcBOD7H9t8BPNz4/WEAX21RHF8G8B+bPB+bAdzV+L0LwBsADjR7TiJxNHVOABiAzsbveQDPAbgXwPcBfLZh/98A/u2NHLcVV/Z7ALzp7qe93mf+MQD3tyCOluHuzwJ4b8eC+1Hv0gs0qVsviaPpuHu/u7/U+H0M9U5IW9HkOYnE0VS8zrJ3dG5Fsm8FcG7O/1vZmdYBPGlmL5rZoRbFcI0+d7+2a8RFAOFWL83hITM71rjNX/GPE3Mxs12oN0t5Di2ck/fEATR5Tlaio3PqC3Qfcfe7AHwSwB+a2UdbHRBQf2dHrNfSyvItAHtR3xCkH8DXmnViM+sE8AMAX3D30bm+Zs5JII6mz4kvoaMzoxXJfgHA9jn/p51pVxp3v9D4OQDgR2htm61LZrYZABo/B1oRhLtfarzQagC+jSbNiZnlUU+w77r7Dxvmps9JKI5WzUnj3MO4wY7OjFYk+wsA9jdWFgsAPgvgx80Owsw6zKzr2u8AfgfA8fioFeXHqHfpBVrYrfdacjX4DJowJ2ZmqDcsPenuX5/jauqcsDiaPScr1tG5WSuM71lt/BTqK51vAfhPLYphD+pKwMsATjQzDgDfQ/12cBb1z14Por5B5tMATgH4GYDeFsXxFwBeAXAM9WTb3IQ4PoL6LfoxAEcb/z7V7DmJxNHUOQFwO+odm4+h/sbyn+e8Zp8H8CaA/wugeCPH1ddlhUiE1BfohEgGJbsQiaBkFyIRlOxCJIKSXYhEULILkQhKdiES4f8D82v8ZoTvHWoAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "new_dict2={v : k for k, v in trainset.class_to_idx.items()}\n",
    "import random\n",
    "for i in range(10):\n",
    "    a=random.choice(list(range(testset.data.shape[0])))\n",
    "    print(a)\n",
    "    show_pic(testset.data[a],new_dict2[testset.targets[a]])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 测试集也看了  这就是个有标签的分类问题"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<torch.utils.data.dataloader.DataLoader at 0x7f8b020c3908>"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "testloader = torch.utils.data.DataLoader(testset, batch_size=BATCH_SIZE, shuffle=False)\n",
    "testloader"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 4、定义好网络和优化器"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 定义一个神经网络"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "__main__.AlexNet"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 定义神经网络  AlexNet是5个卷积层\n",
    "class AlexNet(nn.Module):  # 训练 ALexNet\n",
    "    def __init__(self):\n",
    "        super(AlexNet, self).__init__()\n",
    "        # 五个卷积层\n",
    "        self.conv1 = nn.Sequential(  # 输入 32 * 32 * 3\n",
    "            nn.Conv2d(in_channels=3, out_channels=6, kernel_size=3, stride=1, padding=1),  # (32-3+2)/1+1 = 32\n",
    "            nn.ReLU(),\n",
    "            nn.MaxPool2d(kernel_size=2, stride=2, padding=0)  # (32-2)/2+1 = 16\n",
    "        )\n",
    "        self.conv2 = nn.Sequential(  # 输入 16 * 16 * 6\n",
    "            nn.Conv2d(in_channels=6, out_channels=16, kernel_size=3, stride=1, padding=1),  # (16-3+2)/1+1 = 16\n",
    "            nn.ReLU(),\n",
    "            nn.MaxPool2d(kernel_size=2, stride=2, padding=0)  # (16-2)/2+1 = 8\n",
    "        )\n",
    "        self.conv3 = nn.Sequential(  # 输入 8 * 8 * 16\n",
    "            nn.Conv2d(in_channels=16, out_channels=32, kernel_size=3, stride=1, padding=1),  # (8-3+2)/1+1 = 8\n",
    "            nn.ReLU(),\n",
    "            nn.MaxPool2d(kernel_size=2, stride=2, padding=0)  # (8-2)/2+1 = 4\n",
    "        )\n",
    "        self.conv4 = nn.Sequential(  # 输入 4 * 4 * 64\n",
    "            nn.Conv2d(in_channels=32, out_channels=64, kernel_size=3, stride=1, padding=1),  # (4-3+2)/1+1 = 4\n",
    "            nn.ReLU(),\n",
    "            nn.MaxPool2d(kernel_size=2, stride=2, padding=0)  # (4-2)/2+1 = 2\n",
    "        )\n",
    "        self.conv5 = nn.Sequential(  # 输入 2 * 2 * 128\n",
    "            nn.Conv2d(in_channels=64, out_channels=128, kernel_size=3, stride=1, padding=1),  # (2-3+2)/1+1 = 2\n",
    "            nn.ReLU(),\n",
    "            nn.MaxPool2d(kernel_size=2, stride=2, padding=0)  # (2-2)/2+1 = 1\n",
    "        )  # 最后一层卷积层，输出 1 * 1 * 128\n",
    "        # 全连接层\n",
    "        self.dense = nn.Sequential(\n",
    "            nn.Linear(128, 120),\n",
    "            nn.ReLU(),\n",
    "            nn.Linear(120, 84),\n",
    "            nn.ReLU(),\n",
    "            nn.Linear(84, 10)\n",
    "        )\n",
    "\n",
    "    def forward(self, x):\n",
    "        x = self.conv1(x)\n",
    "        x = self.conv2(x)\n",
    "        x = self.conv3(x)\n",
    "        x = self.conv4(x)\n",
    "        x = self.conv5(x)\n",
    "        x = x.view(-1, 128)\n",
    "        x = self.dense(x)\n",
    "        return x\n",
    "AlexNet#神经网络定义好了"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 这个模型代码可以保存 以后搭网络困难可以用这个 可以在这个基础上修改任何网络"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 可能工程师80%的时间都是在处理数据以及等结果的这个过程!\n",
    "### 目前的数据都是用的torchvision里面自带的数据，以后要自己写数据集"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "AlexNet(\n",
       "  (conv1): Sequential(\n",
       "    (0): Conv2d(3, 6, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
       "    (1): ReLU()\n",
       "    (2): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\n",
       "  )\n",
       "  (conv2): Sequential(\n",
       "    (0): Conv2d(6, 16, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
       "    (1): ReLU()\n",
       "    (2): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\n",
       "  )\n",
       "  (conv3): Sequential(\n",
       "    (0): Conv2d(16, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
       "    (1): ReLU()\n",
       "    (2): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\n",
       "  )\n",
       "  (conv4): Sequential(\n",
       "    (0): Conv2d(32, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
       "    (1): ReLU()\n",
       "    (2): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\n",
       "  )\n",
       "  (conv5): Sequential(\n",
       "    (0): Conv2d(64, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
       "    (1): ReLU()\n",
       "    (2): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\n",
       "  )\n",
       "  (dense): Sequential(\n",
       "    (0): Linear(in_features=128, out_features=120, bias=True)\n",
       "    (1): ReLU()\n",
       "    (2): Linear(in_features=120, out_features=84, bias=True)\n",
       "    (3): ReLU()\n",
       "    (4): Linear(in_features=84, out_features=10, bias=True)\n",
       "  )\n",
       ")"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "### 然后创建优化器\n",
    "net_SGD = AlexNet() #这个名字是自己起的，因为我后面要用这个SGD优化，所以这里就起了一个带SGD的名字\n",
    "net_SGD"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SGD (\n",
       "Parameter Group 0\n",
       "    dampening: 0\n",
       "    lr: 0.01\n",
       "    momentum: 0\n",
       "    nesterov: False\n",
       "    weight_decay: 0\n",
       ")"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 初始化优化器  拿到上面的定义好的net_SGD以后，\n",
    "opt_SGD = torch.optim.SGD(net_SGD.parameters(), lr=0.01)\n",
    "opt_SGD"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "CrossEntropyLoss()"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 定义损失函数\n",
    "loss_function = nn.CrossEntropyLoss()  # 交叉熵损失\n",
    "loss_function"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[]"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 记录training时神经网络的loss值\n",
    "losses_history =[]\n",
    "losses_history"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[]"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 记录training时不同神经网络的top1Acc值\n",
    "top1Acc_history =[]\n",
    "top1Acc_history"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 5、训练并记录训练的LOSS和准确率"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 使用测试数据测试网络 预测之后 拿预测值和标签值进行比较 算一下准确率\n",
    "def Accuracy():\n",
    "    correct = 0\n",
    "    total = 0\n",
    "    with torch.no_grad():  # 训练集中不需要反向传播\n",
    "        for data in testloader:\n",
    "            images, labels = data\n",
    "            images, labels = images.to(device), labels.to(device)  # 将输入和目标在每一步都送入GPU\n",
    "            outputs = net_SGD(images)#把image送如网络 得到输出\n",
    "            _, predicted = torch.max(outputs.data, 1)  # 返回每一行中最大值的那个元素，且返回其索引\n",
    "            total += labels.size(0)# 反正这里就是测试 标签值和预测值是不是相等   算一下准确率\n",
    "            correct += (predicted == labels).sum().item()\n",
    "    return 100.0 * correct / total"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch: 1 Training...\n"
     ]
    },
    {
     "ename": "KeyboardInterrupt",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-47-ec1d20c7d2e2>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      6\u001b[0m         \u001b[0minputs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlabels\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdata\u001b[0m  \u001b[0;31m# 取数据\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      7\u001b[0m         \u001b[0minputs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlabels\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0minputs\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mto\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdevice\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlabels\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mto\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdevice\u001b[0m\u001b[0;34m)\u001b[0m  \u001b[0;31m# 将输入和目标在每一步都送入GPU\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 8\u001b[0;31m         \u001b[0mopt_SGD\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mzero_grad\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m  \u001b[0;31m# 将梯度置零\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      9\u001b[0m         \u001b[0;31m# 训练\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     10\u001b[0m         \u001b[0mnet\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnet_SGD\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mto\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdevice\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/.local/lib/python3.6/site-packages/torch/optim/optimizer.py\u001b[0m in \u001b[0;36mzero_grad\u001b[0;34m(self, set_to_none)\u001b[0m\n\u001b[1;32m    215\u001b[0m                             \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    216\u001b[0m                                 \u001b[0mp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgrad\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrequires_grad_\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 217\u001b[0;31m                             \u001b[0mp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgrad\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mzero_\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    218\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    219\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mstep\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mclosure\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
     ]
    }
   ],
   "source": [
    "# 把数据输入网络，然后进行一下训练  并把LOSS的趋势看一看\n",
    "for epoch in range(nEpochs):\n",
    "    running_loss = 0.0\n",
    "    print('Epoch:', epoch + 1, 'Training...')\n",
    "    for i, data in enumerate(trainloader, 0):\n",
    "        inputs, labels = data  # 取数据\n",
    "        inputs, labels = inputs.to(device), labels.to(device)  # 将输入和目标在每一步都送入GPU\n",
    "        opt_SGD.zero_grad()  # 将梯度置零\n",
    "        # 训练\n",
    "        net = net_SGD.to(device)\n",
    "        outputs = net_SGD(inputs)\n",
    "        loss = loss_function(outputs, labels).to(device)\n",
    "\n",
    "        loss.backward()  # 反向传播\n",
    "        opt_SGD.step()\n",
    "\n",
    "        running_loss += loss.item()\n",
    "        if i % numPrint == 999:  # 每 numPrint 张图片，打印一次\n",
    "            losses_history.append(running_loss / numPrint)\n",
    "            print('epoch: %d\\t batch: %d\\t loss: %.6f' % (epoch + 1, i + 1, running_loss / numPrint))\n",
    "            running_loss = 0.0\n",
    "            top1Acc = Accuracy()\n",
    "            print('Accuracy of the network on the 10000 test images: %d %%' % top1Acc)\n",
    "            top1Acc_history.append(top1Acc)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "####  我这里及时的中止了  因为阿里云这个计算力太弱了"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# for i, l_his in enumerate(losses_history):\n",
    "plt.plot(losses_history, label='SGD')\n",
    "plt.legend(loc='best')\n",
    "plt.xlabel('Steps')\n",
    "plt.ylabel('Loss')\n",
    "plt.ylim((0, 3))\n",
    "plt.show()\n",
    "\n",
    "# for i, t_his in enumerate(top1Acc_history):\n",
    "plt.plot(top1Acc_history, label='SGD')\n",
    "plt.legend(loc='best')\n",
    "plt.xlabel('Steps')\n",
    "plt.ylabel('Top1Acc')\n",
    "plt.ylim((0, 100))\n",
    "plt.show()\n",
    "###这里是打印出准确率和LOSS\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "model_name = 'SGD_my' + '_model.pkl'\n",
    "save_path = './' + model_name\n",
    "torch.save(net, save_path)\n",
    "#保存一下模型\n"
   ]
  }
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